computer science phd uoa

Doctor of Philosophy in Computer Science

This course is available

Level of Study

Doctoral Degree

Next start date

Expected Jul 2024

University of Auckland

The PhD is a globally recognised postgraduate research degree and the highest level of degree you can achieve. PhD students are critical, curious, creative thinkers who undertake original research over at least 3 years.

This course is also offered at overseas locations.

Computer Science is the study of computers and computer concepts: their systems, design, development and use.

Computer technology, it seems, is everywhere in today’s world. It has become an ever-growing part of human life, affecting many aspects of a person’s day. Computer scientists have an impact on how our society advances by developing and maintaining these systems: whether it be for our home, work, learning or entertainment environments.

We’ll cover:

• How information is stored in computers (data structures and management)
• How computers are told what to do (algorithms and programming languages)
• How systems work (computer architecture and system software)
• How computers are connected (data communications, networks and hypermedia
• Some ways that computers can be used (applications)
• What computers can do, and their limitations (computability and complexity theory).

If you’re interested in a relevant and dynamic field of study, and working in an industry where there is a real demand for workers, then Computer Science is for you.

Where can Computer Science take you?

Computer Science graduates can find careers in an ever-widening variety of industries and roles. Here’s a selection:

• Business analyst
• Front end developer
• Information architect
• Network engineer
• UX developer
• Web and web scripting developer

Programme structure

The University of Auckland PhD is a three-to-four year full-time advanced research degree.

On possible to the PhD programme, you will be enrolled provisionally. By the end of your first year, you must meet a number of goals to be confirmed into the PhD programme, including developing a full thesis proposal. After confirmation, you will continue to work on your research before submitting your thesis for examination after three years and within four years of your start date. The examination process includes an oral exam.

As part of your PhD study, you can take part in our doctoral skills programme, designed to help you achieve success in your research and develop skills for your future. We have a dedicated postgraduate careers advisor, and our library service includes specialist subject librarians to help you in your research. The central School of Graduate Studies provides dedicated support and advice for the doctoral community.

Where could this programme take you?

Our PhD graduates are successful in a wide range of fields and careers, as well as the traditional academic research career path. The PhD gives you extensive specialist and transferable skills, which are sought after in many sectors, including industry and government, and can even help you to develop entrepreneurship skills to run your own start up. With focused development opportunities available throughout your PhD study, and dedicated postgraduate career advice, we can help you to explore and meet your own career goals, no matter what direction you take.

Entry criteria

Masters-level qualification or the equivalent of a bachelors degree with honours

Evidence of significant research, usually undertaken as part of prior study

English language requirements

• IELTS (Academic) - Overall score of 6.5 and no bands below 6.0
• Internet-based TOEFL (iBT) - Overall score of 90 and a writing score of 21
• Paper-based TOEFL - Overall score of 68 and a writing score of 21
• C1 Advanced (previously - Cambridge English: Advanced (CAE)) - Overall score of 176 and no bands below 169
• C2 Proficiency (previously - Cambridge English Proficiency (CPE)) - Overall score of 176 and no bands below 169
• University of Auckland Foundation Certificate in English for Academic Purposes (FCertEAP) - Grade of B-
• University of Auckland English Pathway for Postgraduate Studies (EPPS) - Grade of B-
• Pearson Test of English (PTE) Academic - Overall score of 58 and no PTE Communicative score below 50
• Michigan English Language Assessment Battery (MELAB) - 85
• Trinity College London Integrated Skills in English (ISE) - ISE III with a pass in all 4 components
• LanguageCert - International ESOL - C1 Expert (LRWS) with a high pass overall and no less than a pass in each skill OR C2 Mastery with a pass overall and no less than a pass in each skill
• AEMG English for Academic Purposes Direct Entry Program (AEAP DEP) Final Exam - Overall score of 70% with no section below 65%
• English New Zealand Accredited Pathway Assessment - Assessment Level 3 overall and no skill below Level 2

Studying in NZ

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We have 5 University of Auckland Computer Science PhD Projects, Programmes & Scholarships

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University of Auckland Computer Science PhD Projects, Programmes & Scholarships

"chatgpt" for astronomical light curves, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Competition Funded PhD Project (Students Worldwide)

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Untangling Minor Planet Families

Spacecraft trajectory optimisation, space optical communications, integrating data-driven methodologies and model reduction for the control of complex networks, funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

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Lab Director

Senior Lecturer Email: [email protected] Bio: Jiamou Liu is a Senior Lecturer at the School of Computer Science, The University of Auckland. He was a Senior Lecturer at Auckland University of Technology between 2011 and 2015. Jiamou obtained a PhD in Computer Science from the University of Auckland, and worked as a research associate in University of Leipzig between 2009 and 2010 and Paris Diderot University (Paris 7) in 2013. He was a research intern at Microsoft Research Asia in 2008. During his PhD, he was a visiting student at Cornell University and National University of Singapore. Originally trained as a theoretical computer scientist, Jiamou’s current research is in Artificial Intelligence. He is interested in both the theoretical and the applied aspects of artificial intelligence. In particular, his work revolves around the structural analysis and algorithmic studies of social networks, multiagent systems, machine learning, as well as natural language processing. His recent research also connects to health-care, real estate data mining, data privacy, computational social science, and spatial-temporal data analysis with application in traffic flow prediction.

PhD Students

2020.02 – Present Email: [email protected] Bio: Qiming received a Bachelor of Science (Honours) degree with First Class Honours in Computer Science from the University of Auckland in 2019. His research interests lie in the intersection between natural language processing and question answering. Recently his research mainly focuses on machine reasoning. His main research activities involve natural language understanding, natural language inference, and soft reasoning; topics of interest include problem decomposition, knowledge acquisition, explanation, entailment, and generate QA. He is also interested in the graph-based neural network.

Pengqian Han

2021.10 – Present Email: [email protected] Bio: I got my master’s degree in aeronautical engineering from Beihang University in 2018 and got my bachelor’s degree from Hebei University of Technology in 2014. I have been an engineer in China Mobile Group Tianjin Company from 2018 to 2021. My main research interests are machine learning, deep learning, and graph-based neural network.

2020 – Present Email: [email protected] Bio: Weidong received a degree of Bachelor of Science (Honours) with First Class Honours in Computer Science from the University of Auckland in 2019. She received a Bachelor of History from Capital Normal University in 2013. Her research interests include social network analysis, multi-agent systems and reinforcement learning.

Xiaoxuan Li

2020 – Present Email: <> Bio: I received my honours (First Class) degree in computer science from The University of Auckland in 2019 and received my bachelor’s degree from The Tianjin University of Technology in 2015. My primary scientific interests focus on exploring Natural Language Process (NLP) technology to facilitate Robotic Process Automation (RPA), in particular in discovering business process flow from natural language texts.

2019. 11 – Present Email: [email protected] Bio: I received my bachelor of mathmatics and master of engineering from Beijing Institute of Technology. My major interest lies in the network structures and dynamics. Recently my research focuses on the core/periphery structures. I am also interested in the theoretical analysis of machine learning, graph-based neural network, and so on.

2020 – Present Email: [email protected] Bio: Lin Ni, a practitioner and academic, is keen to turn AI technologies into concrete value. She received a B.E. degree from Northeastern University (China) in 2010, and a Postgraduate Diploma in Computer Science from the University of Auckland in 2017. She lands technologies and provides AI solutions to address the challenges in the industry, dabbled in Car Navigation systems, Recommendation systems, Multi-agent systems, and Natural Language Processing projects. Chatbots attract her focus in recent years, especially chatbots in the health and medical domain.

Qianqian Qi

2019.9 – Present Email: [email protected] Bio: I received my M.Sc. degree specialised in Communications and Signal Processing from Imperial College London in 2014, and my B.Eng. degree major in Electronic and Information Engineering from Dalian University of Technology in 2013. After that, I worked as a software engineer in Singapore for more than 4 years. I am now doing research in the field of Natural Language Processing, mainly on natural language understanding and generation.

To start PhD Email: <> Bio: I received my master degree in computer science from Southwest University in 2019 and bachelor degree from Chongqing Normal University in 2016. My research interests include social network analysis, opinion dynamic, multi-agent systems and deep learning.

2020 – Present Email: <> Bio: I received my master degree in computer science and technology from the Beihang University in 2020, and bachelor in software engineering from Dalian University of Technology in 2017. My main research interests are machine learning and deep learning, especially Graph-based models and Self-supervised models. Recently, I am focused on constructing hybrid models combining anchor-based graph and neural networks to solve real-world problems with Self-supervised learning.

2019.12 – Present Email: [email protected] Bio: I received both my Honours (First Class) and Bachelor of Science Degrees in Computer Science from The University of Auckland. My main research interest lies in network structures, optimization problems, and discrete space evaluation and optimization. Recently, I am focused on core-periphery network structures.

2019.12 – Present Email: [email protected] Bio: I got my master degree of computer science from the University of Auckland in 2018 and got my bachlor degree from Nanjing University in 2016. My main research interests are machine learning, deep learning, graph-based neural network and computer vision. I also do reserach related to artificial intelligence in healthcare and software engineering.

2022.02 – Present Email: [email protected] Bio: I received a degree of Master of Professional Studies with First Class Honours in Data Science, the Graduate Diploma in Science from the University of Auckland in 2021, and the Bachelor degree from the Southwest University in 2020. My personal research interests lie in the reinforcement learning and the multi-agent systems. In detail, I am interested in the challenges in both cooperative and non-cooperative settings in adapting reinforcement learning to multi-agent systems.

Xianda Zheng

2022.10 – Present Email: [email protected] Bio: Xianda received M.Eng degree in Computer Science from Southeast Univercity in 2021 and received B.Eng degree in Computer Science from Changchun University of Science and Technology in 2018. His research interests include Knowledge Graph representation learning and reasoning.

Zijian Huang

2023.04 – Present Email: [email protected] Bio: I received both my Master of Science (First Class) and Bachelor of Science degrees in computer science from the University of Auckland. My primary research interests include knowledge graph representation learning, multi-hop query reasoning over knowledge graphs, and natural language question answering.

2018.09 – 2023.07 Email: [email protected] Bio: I received my bachelor degree from Wuhan Institute of Technology and master degree from Wuhan University of Technology. I have been a lecturer in Guangdong University of Petrochemical Technology from 2015 to 2018. My research interests include social network analysis, especially network embedding.

2018. 08 – 2023. 04 Email: [email protected] Bio: I received both my Honours (First Class) and Bachelor of Science Degrees in Computer Science from The University of Auckland. My research interests lie broadly in Natural Language Processing with my recent research focusing on the intersection between Psychology, Artificial Intelligence and Natural Language Processing.

2018. 11 – 2022. 04 Email: [email protected] Bio: I am currently a research fellow with the School of Computer Science, The University of Auckland . I obtained my PhD in computer science from The University of Auckland , where I was supervised by Dr. Jiamou Liu and Prof. Bakhadyr Khoussainov . I received the First Class Honours degree in computer science from The University of Auckland in 2018. Before coming to New Zealand, I received the B.S. in computer science from Beijing Institute of Technology in 2017. My research interests span a spectrum of (inverse) reinforcement learning, computational game theory and multi-agent systems. Lately, my research focus is on applying reinforcement learning in large-scale multi-agent games, natural language processing and automatic reasoning. When I am not spending time on research and teaching, I can be found as a hiker and photography lover. I have hiked to many famous routes in many cities and towns across China and New Zealand. The collections of pictures that I took in the hike-way can be found in my personal online gallery .

Mengxiao Zhang

2017. 04 – 2021. 11 Email: [email protected] Bio: I hold Bachelor’s degree of Management in Information Management and Information Systems and Master’s degree of management in Management Science and Engineering. My research interests are algorithmic mechanism design and economics of security and privacy. My current research focus is incentive mechanisms for data pricing and privacy preservation.

Sajjad Hassanpour

Email: [email protected] Bio: Sajjad Hassanpour is a Ph.D. student in Construction Engineering at the University of Auckland. He received a bachelor’s degree in civil engineering at Iran University of Science and Technology in 2010, and his M.Sc. degree in transportation planning-at the Trabiat Modares University of Iran in 2012. During his Ph.D. in transportation planning, as he was interested in computer science, he started working on agent-based modeling and computer-based simulation models. Therefore, he started a new Ph.D. at the University of Auckland, focusing on agent-based building earthquake evacuation simulation. He is interested in pedestrian dynamics, multiagent systems, and machine learning.

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We are part of the School of Computer Science at University of Auckland .

PhD in Computer Science

Ranked among the top 25 graduate programs in the u.s..

Dive into AI, human-centered computing, or another area of computer science that intrigues you. Develop your research, teaching, and leadership skills. And work with the brightest minds in the field to advance computing for the common good with UMass Amherst’s PhD in computer science.

Designed to prepare you for teaching, research and leadership positions, our PhD program ranks among the top 25 in the nation and features world-renowned faculty, interdisciplinary research, and a spirit of collaboration that attracts the brightest students and faculty. You’ll also have the opportunity to conduct groundbreaking research with the industry-leading technology companies we collaborate with.

PhD in computer science course requirements:

• Six core courses in systems, AI and theory (18 credits)
• Portfolio/comprehensive exam
• Dissertation research (18 credits)
• Doctoral thesis proposal and defense

The PhD Student Handbook provides information and answers questions on a range of topics, such as assistantships, dissertation process, forming a committee, and more.

Related offerings

Students interested in our PhD in Computer Science may also be interested in these other offerings.

• MS in Computer Science — on Campus
• MS in Computer Science — Online

Benefits list

Faculty Expertise and Academic Rigor

Our PhD program features advanced coursework taught by award-winning faculty working at the forefront of computing’s fastest-growing fields. Our curriculum prepares you to take on today’s biggest societal challenges across many disciplines — from technology and finance to health care and the arts.

Student Experience

Find your place here with supportive 1:1 advising and community events like  HackHer413 . Build leadership skills and connections by participating in one of over a dozen active student organizations, including the ever-popular  BUILD UMass  and  UMass Robotics .   

Featured classes

This course introduces graduate and undergraduate students to concepts, practices, and tools for conducting effective research. Students will learn basic methods for activities such as reading technical papers and selecting research topics.

Principles underlying the design and analysis of efficient algorithms. Topics to be covered include: divide-and-conquer algorithms, graph algorithms, matroids and greedy algorithms, randomized algorithms, NP-completeness, and approximation algorithms

Intelligent visual computing is an emerging new field that seeks to combine modern trends in machine learning, computer graphics, and computer vision to intelligently process, analyze, and synthesize 2D/3D visual data. 

Featured faculty

Narges mahyar.

Human-computer interaction (HCI), information visualization, digital civics, social computing, crowdsourcing, and design thinking.

Ina Fiterau Brostean

Machine learning, ensembles, multimodal data fusion, hybrid models, health care applications.

Evangelos Kalogerakis

Computer graphics and vision, geometric modeling, 3D deep learning, animation, shape analysis and synthesis, scene modeling, 3D reconstruction.

Marco Serafini

Database systems, distributed systems, data science.

Alexandra Meliou

Data management, data quality, provenance, causality, explanations, data accessibility, and algorithmic bias.

In the spotlight

“Because of the flexibility that CICS offers, I’ve been able to chart my own path. I conduct research on the privacy implications in devices that use machine learning. UMass has such a friendly and collaborative nature with people of all different backgrounds that it really does feel like home."

Application information & deadlines

Fall admission, december 15, 2022.

Application deadline.

Computer Science

With a UMass Amherst computer science degree you’ll have the skills to apply advanced computer science techniques to solving today’s most complex problems.

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• Graduate Students Receive Special Recognition for Outstanding Work

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• TUM Graduate School
• Technical University of Munich

Apple Scholars in AI/ML 2025 - Call for Applications

Aktuelles | 31.07.2024

Apple has announced the PhD Fellowships in AI/ML 2025 program. Nominees should be pursuing research in one or more of the following research areas:

• Privacy preserving machine learning
• Human centred AI
• AI for ethics and fairness
• AI for accessibility
• AI for health and wellness
• ML algorithms and architectures
• Embodied ML
• Speech and natural language
• Computer vision
• Information retrieval, ranking and knowledge
• Data-centric AI

What is included?

The fellowship award comprises

• A generous stipend for (2) academic years to help with living expenses and related expenses
• $5,000 USD travel grant each year to support research-related travel and associated expenses 
• Mentorship with an Apple researcher 
• Potential Internship opportunities during their fellowship (subject to student status and relevant employment law). 

The monetary award will be directed to the nominating universities in the form of an unrestricted gift with the intention to fully support the University’s successful nominee(s), disbursed annually Award amounts will vary by country.

The award of the Apple Scholar in AI/ML PhD fellowship does not represent an employer/ employee relationship between Apple and the receiving university and/or selected student.

How to apply?

Nomination by TUM Graduate School only . 

In order to apply, please submit the complete application documents in separate PDF files to Charlotte Janosa, [email protected],  until 31 July 2024 .  Applications must be submitted by the primary thesis supervisor .

After the application deadline, the TUM Graduate School Prize Committee shall nominate the 3 most promising candidates for the Apple PhD Fellowship 2025 in AI/ML.  

Application documents

• CV and publication list 
• Research Abstract (200 word maximum) 
• Research statement covering past work and proposed direction for next 2 years (5 page maximum, including citations, in a legible font size) clearly stating the hypothesis and expected contributions to the chosen research area. Personally identifiable information is redacted for phase one reviewers. We recommend not including personally identifiable information in the main body of the research statement in order to maintain research statement clarity for reviewers of the redacted copy. 
• Two letter of recommendation, one from current supervisor (1 page maximum per letter) 
• Transcripts of current and previous academic records (Bachelor, Masters) 

Documents must be submitted as PDF’s with the file naming convention: “Last name, First name: Research Statement”; “Last name, First name: Resume”; “Last name, First name: Letters of recommendation” etc.

Eligibility

Please note, in order to be eligible

• Applicant must be fully registered on the doctoral candidacy list of their School (in DocGS) and be a full member in the TUM Graduate School at the time of application and expect to be enrolled through the end of the 2025/2026 academic year
• Applicant should be entering their last 2-3 years of the doctorate as of Fall 2024.
• Applicant must not hold an equivalent industry-sponsored full fellowship while they are an Apple Scholar in AI/ML (Fall 2025 to Summer 2028)

Contact:  Charlotte Janosa | janosa(at)zv.tum.de | 089 289 10624, TUM Graduate School, Boltzmannstr. 17, 85748 Garching b. München

All information submitted by TUM will only be used by Apple for the purposes of conducting the PhD Fellowship in AI/ML program, and will at all times be handled in accordance with Apple’s Privacy Policy .

Machine Learning Seminar by Prof. Nikola K Kasabov – Spatio-Temporal Learning and Spatio-Temporal Associative Memories in Bio/neuro systems, Mathematics and Brain-inspired Neurocomputation

Machine learning seminar by dr. lia morra – neuro-symbolic ai advancements in computer vision, machine learning seminar by dr. pablo montero manso – large pre-trained models achieve near-optimal time series forecasting, machine learning seminar by prof. william stafford noble – deep learning applications in mass spectrometry proteomics and single-cell genomics, a new supercomputer for scaling up machine learning and artificial intelligence at waipapa taumata rau / the university of auckland, machine learning seminar by prof. christian s. jensen – new vehicle routing paradigms enabled by big vehicle trajectory data, machine learning seminar by dr. xia ning – deep generative models for molecule optimization, machine learning seminar by alex gavryushkin – online algorithms for evolutionary and systems biology, machine learning seminar by pat langley – computational scientific discovery: heuristic search for communicable laws and models, ml student seminar on dec 2 2021: adversarial learning on time series forecasting.

Adversarial Learning: Robust and Reliable Machine Learning Models

Kia Ora! I am Luke Chang and I am passionate about building more reliable machine learning models, an artificial intelligence people can trust. I started my machine learning journey by designing a transmission controller for an engine in my third-year undergraduate project. I was fascinated by how artificial intelligence can be integrated into everyday systems. When I learnt a state-of-the-art machine learning model is vulnerable to malicious attacks, even when trained on a large amount of...

Knowledge-Driven Text Generation

I am Beryl Qi and my research topic is ‘Knowledge-Driven Text Generation’. Natural language generation (NLG) is an important research direction in the field of natural language processing. This technology can be applied to various information processing tasks, such as QA (Question Answering), IE (Information Extraction), PD (Problem Decomposition), etc. The wide application of NLG benefits from its learning and processing capabilities when facing to explosion of data. The overall system we...

Identification and Mitigation of Selection Bias

Kia Ora! I am Katharina Dost, a PhD student in my second year with the School of Computer Science. My research topic is “Identification and Mitigation of Selection Bias” and I would like to use this post to talk about my research and my experiences, so read on! Our world runs on data. We gather whatever we can and use it to answer a variety of questions. These can be something as simple as “What is the average age of my customers?” but also as critical as “Which treatment is best suited for...

University of Auckland Machine Learning Group

Machine Learning is a field of Computer Science that aims to develop algorithms that learn from experience. This is in contrast to traditional computer science algorithms where behavior is directly coded into the algorithms. Machine Learning algorithms take experience in form of data and observe patterns in these data to make decisions and predictions. Machine Learning became widely popular in recent years and Machine Learning methods are widely used, having applications in every industry...

AskAuckland

Your online help and support centre, postgraduate computer science.

The Department of Computer Science offers subjects mostly within the two general areas of software systems and theory of computing. The department has strong research activities, clustered in several substantial research groups, in algorithmic information theory, artificial intelligence, combinatorics, data compression, data communications and networks, distributed computing, graphics, image processing, logic, multimedia and hypermedia systems, neural networks, parallel computation, programming languages and systems, robotics, software engineering, software security, theory of computation, and visual programming. Other areas of informatics are covered in more depth by other departments in the University. Studies in management science and information systems (within the Business School) specialise in database systems, groupware, and applications of computers to business and management. The Department of Electrical and Electronic Engineering (Faculty of Engineering) has interests in computer systems engineering and software engineering. Under the Master of Professional Studies, our Computer Science Department offers the Data Science specialisation . You can study computer science at postgraduate level in the following programmes:

Coursework programmes Postgraduate Diploma in Science - PGDipSci Bachelor of Science (Honours) - BSc(Hons) Research programmes Master of Science - MSc Doctor of Philosophy - PhD Further information on these programmes is available at: Postgraduate study in Computer Science .

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Graduate Admissions

This AI-generated image showcases what's possible. At the College of Information Science, you can learn how to analyze, manage and lead our transition into an AI-fueled future.

The College of Information Science (InfoSci) offers leading graduate degrees in Data Science , Information Science , and Library and Information Science that prepare you for a wide range of in-demand careers across disciplines and industries.

We are dedicated to your success, delivering a world-class, cross-disciplinary education that empowers you with the skills and knowledge you need to embark upon your information leadership journey.

Graduate applications for Fall 2024 are now closed. Graduate applications for Spring 2025 are open.

Graduate Programs

Master of science in data science (msds).

Ranked the #9 Master's in Data Science program in the country by Fortune , In the 18-month, STEM-designated MSDS, offered on campus and online, you'll get the training you need in data collection, exploration, manipulation and storage, analysis, and presentation to excel and innovative in the data-rich workplace.

Career Possibilities

MSDS graduates are ready to excel in many positions, including data analyst, architect, engineer, modeler, scientist or visualization designer; artificial intelligence engineer; big data engineer; business intelligence analyst or developer; language engineer; machine learning engineer; quality specialist, market research analyst; statistician; and more. Learn more about MSDS career outcomes.

Admission Requirements

• Linear math (college algebra)
• Computer science courses
• Programming experience in such computer languages and/or areas as C, C++, Java, Python, Data Structures, HTML, CSS, SQL, Web Programming, R, Julia, MATLAB, TensorFlow, JavaScript, Scala, Git/GitHub and/or SAS
• View MSDS admissions information

Example Courses

• INFO 523: Data Mining and Discovery
• INFO 526: Data Analysis and Visualization
• INFO 502: Data Ethics
• INFO 529: Applied Cyberinfrastructure Concepts
• INFO 531: Data Warehousing and Analytics in the Cloud
• INFO 580: Data Standards for the Semantic Web
• View MSDS curriculum and courses  

Explore the MS in Data Science

MASTER OF SCIENCE IN INFORMATION SCIENCE (MSIS)

Ranked the #11 program in the nation for machine learning by TechGuide, the STEM-designated, 18-month MSIS is a transformative, interdisciplinary journey that gives you the advanced skills you need to implement information methods across organizations and industries. Two subplans are offered: Human-Centered Computing and Machine Learning .

MSIS graduates are ready to excel in many positions, including application or systems analyst, cybersecurity analyst, database administrator, data scientis or engineer, digital repository manager, information architect, information security manager, information technology manager, software developer or programmer, web programmer or producer, and more. Learn more about MSIS career outcomes.

• View MSIS admissions information
• INFO 505: Foundations of Information
• INFO 516: Introduction to Human Computer Interaction
• INFO 521: Introduction to Machine Learning
• INFO 524: Virtual Reality
• INFO 550: Artificial Intelligence
• INFO 551: Game Development
• View MSIS curriculum and courses  

Explore the MS in Information Science

MASTER OF ARTS IN LIBRARY & INFORMATION SCIENCE (MLIS)

Offered on campus or online, the two-year MLIS prepares you for an exceptional career in libraries, museums and archives, as well as government and business. Accredited by the American Library Association, the MLIS offers specialties in Academic Librarianship, Archival Studies, Digital Curation, Legal Information, Public Librarianship and Special Librarianship.

MLIS graduates are ready to excel in many positions, including public, school, academic, health science or legal librarian; library manager; health information specialist; media specialist; research analyst; researcher; data analyst; metadata analyst; archivist; copyright specialist; curatorial specialist; digital curator; deigital reposity specialist; digital services manager; geospatial specialist; information architect; information officer; and more. Learn more about MLIS career outcomes.

• A bachelor’s degree from the University of Arizona or from an accredited college or university recognized by the University of Arizona
• A grade point average of 3.0 or higher
• A GRE   is not required. You may still submit one if you choose to.
• View MLIS admissions information
• LIS 504: Foundations of Library and Information Services
• LIS 506: Research Methods for Library and Information Professionals
• LIS 515: Organization of Information
• LIS 518: Information Trust, Manipulation and Deception
• LIS 530: Cataloging and Metadata Management
• LIS 535: Business Information
• LIS 540: Introduction to Archives
• LIS 541: Preservation
• LIS 558: Social Justice in Information Services
• View MLIS curriculum and courses  

Explore the MA in Library & Information Science

PHD IN INFORMATION

The PhD in Information, offered on campus, allows you to work with top faculty in the field of information while conducting original research and preparing for a career in academics, government or industry. Leading research in the iSchool focuses on behavior and related human factors of information across sectors, from economics and business to health, from education to art, and beyond. The PhD in Information is typically completed in 4 or 5 years.

Graduates from our doctoral program become creative researchers who can analyze, evaluate and explain a variety of information phenomena across varied domains of research, including the sciences, social sciences, library and information sciences, arts and the humanities. PhD graduates have found successful careers as professors and lecturers, data analysts, data science managers and directors, consultants, research scientists and more. Learn more about PhD in Information career outcomes.

• Admission to the doctoral program is competitive and based on both your abilities and faculty interest and expertise in your proposed area of study.
• The School of Information may deny entry to an otherwise qualified applicant if there is insufficient faculty intellectual overlap for working with that applicant.
• A GRE is not rquired as part of our admissions process.
• An undergraduate or graduate academic record that is indicative of significant achievement (typically a minimum GPA of 3.5 on a 4.0 scale)
• View all PhD in Information admissions and funding information

Suggested Timeline

• Year 1: Complete INFO 507: Information Research Methods; submit plan of study at the end of year 1
• Year 2: Complete course work
• Year 3: Complete comprehensive exams at beginning of Year 3; complete dissertation committee appointment form after passing comprehensives; complete and defend dissertation proposal by end of Year 3
• Years 4-5: Conduct dissertation research and writing; defend dissertation by end of Year 4 or Year 5 (depends on progress, funding and support from faculty advisor)
• View PhD in Information curriculum and degree requirements  

Explore the PhD in Information

Graduate Program Admissions

Prospective graduate students are required to submit application materials directly to the University of Arizona Graduate College . Please follow the procedures described in the application portal. You may access your application as many times as necessary to complete and correct it prior to formal submission. Applications will not be reviewed until all materials have been received.

Master's Application Deadlines

Master of science in data science, main campus.

• Fall Semester: February 1 Currently closed
• Spring Semester: August 1 Currently open
• Summer Term: February 1 Currently closed; not available to international applicants

Arizona Online

• Fall Semester: March 15 Currently closed
• Spring Semester: October 1 Currently open
• Summer Term: March 1 Currently closed

Master of Science in Information Science

Domestic applicants, international applicants, master of arts in library and information science, online campus, global campus.

• Fall Semester: February 15 Currently closed
• Spring Semester: September 1 Currently open

PhD Application Deadline

Annual deadline for all students.

• Fall 2025: January 20, 2025

Application is currently open.

Frequently Asked Questions

Get answers to the most frequently asked questions about the MSDS, MSIS, MLIS and PhD in Information, as well as the Statement of Purpose across all programs, by viewing our Graduate FAQs:

View Frequently Asked Questions

Connect with Our Graduate Programs Recruiter

Tavia Szostek Manager of Graduate Enrollment and Retention

[email protected] 520-621-4986

Electrical Engineering Graduate Programs

From the optical fibers that connect the world to deep space exploration, many of humanity’s greatest achievements would have been unthinkable without electrical and computer engineers. Our department has played an important role in many of these advancements. 

You can add to our legacy in world-class photonics, high-frequency diodes, and materials science research, and extend our long-standing tradition in computer engineering, devices and circuits, signal and image processing, and communications.

Our co-location with the University of Virginia's School of Medicine enables ECE faculty and students to conduct research in medical imaging, microfluidics, electrophoresis, bio-molecular engineering, and biomedical and cyber-physical data mining. Machine learning and artificial intelligence now influence everything we do, including the design and operation of engineered systems; you can leverage our investments in machine learning research to ride the AI wave.

We aspire to be an inclusive and welcoming place for all, and a hub of innovation where research and teaching go hand-in-hand. Our mission is to prepare the next generation of electrical and computer engineering leaders to solve society’s grand technological challenges and improve quality of life.

Our Graduate Programs

Ph.d. in electrical engineering.

We offer a direct pathway from your bachelor's degree to a Ph.D. If you are admitted to the Ph.D. program, you have the option of earning your M.E. or M.S. degree en route. 

M.S. in Electrical Engineering

The M.S. is a research-based master's that requires a final thesis defense.

M.E. in Electrical Engineering

The M.E. is entirely course based and can be earned in person or online through Virginia Engineering Online .

Resources for Current Students

Uva engineering graduate student resources.

Explore our resources for graduate students, including professional development support and ways to get involved at UVA Engineering.

Academic Planning

Find timelines, forms and guidance for completing your degree on schedule.

EE Graduate Student Handbook

A detailed resource of graduate degree requirements.

EE Degrees are in Demand

Our electrical engineering program is known throughout industry and academia as a source of outstanding engineers and researchers. Our faculty maintain this reputation by continually updating curricula to reflect current scientific and technological advances and requirements and by maintaining a vigorous, interdisciplinary research program that provides staff and graduate students with high-level learning experiences relevant to societal needs.

Recent graduates have joined industry, such as Apple, Amazon, BAE Systems, Booz Allen Hamilton, Corning, Facebook (now Meta), General Electric, General Motors, Google, Halliburton, Intel Research, Microsoft Research, NVIDA Research, Northrop-Grunman, Oracle, Qualcom and Samsung Research. Some chart their path to government research labs including the Air Force Research Laboratory, Army Research Laboratory, NASA, NIST and The U.S. Department of Energy's National Renewable Energy Laboratory.

Others have followed their advisors' path into academe, pursing postdoctoral research and faculty positions in the United States and overseas, including Kuwait University, the National Defense University of Technology, China; the Royal Thai Air Force Academy; Shanghai Jiao Tong University, China; the University of California Davis, University of Florida, University of Illinois at Urbana Champaign, University of New Hampshire, University of Notre Dame, and Virginia Commonwealth University.

Graduate Program Contacts

Beth eastwood-beatty .

GRADUATE APTITUDE TEST IN ENGINEERING 2025

अभियांत्रिकी स्नातक अभिक्षमता परीक्षा २०२५, organising institute: indian institute of technology roorkee.

GATE 2025 TEST PAPERS & SYLLABUS

GATE 2025 will be conducted for 30 test papers. The following table shows the list of papers with codes. Please click the Paper/Code to download the syllabus.

A candidate is allowed to appear either in ONE or UP TO TWO test papers. Please see the page Two-Paper Combination for more details.

The test papers will be in English. Each GATE 2025 paper is for a total of 100 marks, General Aptitude (GA) is common for all papers (15 marks), and the rest of the paper covers the respective test paper syllabus (85 marks). Click here for detailed pattern of the question papers .

Multi-sessional papers: Candidate will be assigned to appear only in one of the sessions for the papers running in multiple sessions.

Computer Science and Information Technology (CS) and Civil Engineering (CE) will be conducted as multi-session papers in GATE 2025. More precisely, they will be two-session papers. This means that the candidates will be assigned to one of the sessions only — either the forenoon session or the afternoon session. The question papers will be different for each session. Test papers are held in multiple sessions when the candidate count is so high that they cannot all appear for the test in the same session. The scores of the candidates will be normalized according to the normalization formula given in Section 13.2 of the Information Brochure.

Candidates must familiarize themselves with the paper code as it is required both during application and examination.

Each candidate should fill ONLY ONE application. If they wish to appear in second paper (from the two-paper combination), they can add respective paper in their original application. In case of Multiple applications, only one will be accepted and remaining applications will be rejected without any refund for the paid fee.

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• MyU : For Students, Faculty, and Staff

hUMNs of Chemistry #17

They/them Professor

Tell us about your journey to the University of Minnesota.

My first visit to the University of Minnesota was during my graduate school years at UW-Madison.  I came to make measurements at the Institute for Rock Magnetism and also ended up meeting the love of my life during that visit.  Fast forward, the love of my life and I live right here in Minneapolis.  

We would love to hear more about your research! What do you hope to accomplish with this work? What is the real-world impact for the average person?

I'm very excited to work on projects related to the environment, green chemistry, and sustainability. We have projects focused on the behavior of iron-bearing minerals in environmental systems, microplastics, biochar, materials for sustainable energy applications, and a brand new project with the Boiteau group focusing on the ocean!  

What courses do you teach? What can students expect to get out of your course?

I have had the great fortune of teaching general chemistry courses, which is great fun (most of the time).  General chemistry is a tough class, and I strive to make the classroom a supportive and welcoming one.  I also take every opportunity to include demonstrations during lecture, including popular Energy and U demonstrations like the screaming gummy bear, methane mamba, and the flaming tube of death.

What do you hope to contribute to the chemistry community at the University?

I hope to do innovative research in the areas of environmental chemistry and sustainability; continue and foster interdisciplinary collaborations; serve as an effective mentor and educator; engage with the broader community through research, outreach, and other activities; and promote diversity, equity, and inclusion at the U of MN and beyond.

When you visit other universities, what do you love to share about our UMN community?

Ongoing commitments to collaborative and interdisciplinary research and to diversity, equity, and inclusion.

What do you do outside of the classroom/lab/office for fun?

I'm sure that most folks know I ride bikes -- a lot. I do some other things, honestly, I do...  I mean, sometimes.

What’s your favorite piece of chemistry/science pop culture media? Why do you love it?

I'm about to teach Chem 1015 (Introductory Chemistry) for the first time, and I think I might have to play this.

What was your very first job?

Mowing lawns

Tell us about who makes up your household (including pets).

Cheryl (human), Dandelion (dog), Bella (cat), and Dubby (cat).

Eric Schulz

Information Technology Professional

Please give a brief description of your role within the UMN Chemistry department.

If it plugs into a wall outlet or sits next to a computer or runs on a laptop and it "used to work before," I'll be asked to have a look at fixing it.

I grew up in Wisconsin and came to the U of MN for a degree in engineering.  As it turns out, I wasn't a very good engineer, but I was able to pick up a number of burgeoning computer skills.  I've got my start in DOS, Novell Netware, hand typing HTML and fixing Apple II's; while steadily learning the workings of past computers to today's iPads, laptops, computers and operating systems. The E-Shop's previous employees have passed on a few skills to me on instrument repair, but I wished I'd paid better attention while they were here!

Do you have a background in or like chemistry? Tell us about it!

I don't have anything other then a year of college Chemistry under my belt.

What professional successes are most important to you?

I was very proud of the web server and services that I ran for a number of years.  I have always felt that supporting the Department of Chemistry and its people with the best of my abilities is a very important part of who I am.

I've a few years left, and I hope to continue to learn about new technological advances.

Burger King cashier!

My family bought our first house a few years ago and I do enjoy fixing it up, both inside and outside.  I enjoy swimming, camping with the Boy Scouts and playing cards with friends.

What non-chemistry interest or activity of yours might surprise department members?

I recently got my certification in scuba diving and went on a diving trip to Florida.  Its probably not too surprising, but I like to learn how to repair cars and appliances.  

My wife, my daughter, my son, my father in-law and three cats!

Casey Wouters

She/her Graduate student

I grew up in the suburbs of Dallas, Texas and pursued my undergraduate studies at the University of Oklahoma. In college, I studied chemistry and worked as an undergraduate researcher in Dr. Charles Rice's lab at OU. I came to UMN directly after finishing my BS in chemistry, with my first time visiting the Twin Cities being the day I arrived with all my stuff to move here (thanks, COVID). 

Are you involved in any student groups? What inspired you to get involved?

I'm currently serving as secretary of Queer Science, a group which does outreach to queer high schoolers in the Twin Cities metro. I love working with this group and think it's so important for young people to be able to see themselves represented in STEM. Additionally, I spend a lot of time working on the Graduate Labor Union and it's related causes. I am on the bargaining committee working with the university on writing our first contract! I became an organizer because I believe in the value of our work as grad students and have found my work with GLU to be incredibly fulfilling. 

Tell us about an important mentor in your academic life?

My undergraduate research adviser Dr. Charles Rice served a pivotal role in my experience at OU and in my grad school application and decision process. Working in his lab gave me amazing hands-on experience and confidence in my research abilities. I even got to conduct my own project for my honors thesis, which was published last year. Having a supportive PI and a lab group that formed its own little family was so important to my time at OU. 

What are your plans after graduation?

After graduation, I hope to stay in the Twin Cities and get a job in the local science industry. I also have a keen interest in science policy and hope to incorporate some element of this into my future career as well. 

I love to craft and read! Name a craft and I have probably tried it, but my favorite is knitting. I like to constantly have a bunch of works in progress at the same time and to also have a book to suit every mood.   

Related news releases

• hUMNs of Chemistry #16
• Professor George Barany retires after a 44 year career at the University of Minnesota
• hUMNs of Chemistry #15
• Hannah Kenagy and Melissa Ramirez join Department of Chemistry
• hUMNs of Chemistry #14
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Computer Science

Compsci 316: cyber security semester 2, city campus.

The course introduces various concepts related to software, system, and network security. It covers a range of topics including attacks on privacy and attack surface, static and dynamic analysis of malware, hardware security, network security and some hot topics including blockchain and bitcoin.

There is a compulsory tutorial every week except Week 1 and Week 7. There are multiple tutorial streams, and you are supposed to join one of them. To choose and join a tutorial stream, click here .

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Learn more about the outstanding work being done by our graduate students.

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Currently open tenure-track faculty, career track faculty, and staff positions.

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Latest News

Dr. Diana Diazh joining UA Department of Computer Science!

Portia Cooper receives 2024-2025 Barry Goldwater Scholarship

Machine Learning & Data Science Foundations

Online Graduate Certificate

Cutting-Edge Curriculum

The power of data grounded in computer science .

Artificial intelligence is transforming how all industries and organizations operate. Now more than ever, there is an increasing demand for data scientists and engineers who can understand and implement machine learning technology. To gain insights from massive data sets, drive efficiency, create technological advancements, and win in the marketplace, organizations need data professionals who can develop powerful algorithms and intelligent machines. 

Offered by CMU’s School of Computer Science, one of the nation’s top universities for learning computational data science, this online certificate equips students with the requisite AI skills to solve real, large-scale data problems across various industries.

Curriculum Overview

After you enroll in the Machine Learning & Data Science Foundations program, you will take six graduate-level, credit-bearing courses. Each course will appear on your Carnegie Mellon transcript with the grade earned.

To earn the certificate, you must successfully complete all courses in the program. If you are only interested in one course, however, you may complete that course only and it will show on your transcript with the grade earned. 

Please note: the Python for Data Science and Foundations of Computational Data Science courses are delivered in two consecutive parts at 6 units each.

The certificate includes the following courses taught by CMU faculty:

Mathematical foundations of machine learning.

Course Number:  10-680

Units:  6 units

Practice the necessary mathematical background for further understanding in machine learning. You will study topics like probability (random variables, modeling with continuous and discrete distributions), linear algebra (inner product spaces, linear operators), and multivariate differential calculus (partial derivatives, matrix differentials). Some coding will be required; ultimately, you will learn how to translate these foundational math skills into concrete coding programs.

Computational Foundations for Machine Learning

Course Number:  10-681

Practice the necessary computational background for further understanding in machine learning. You will study topics like computational complexity, analysis of algorithms, proof techniques, optimization, dynamic programming, recursion, and data structures. Some coding will be required; ultimately, you will learn how to translate these computational concepts into concrete coding programs.

Python for Data Science

Course Numbers:  11-604 & 11-605

Units:  6 units each

Master the concepts, techniques, skills, and tools needed for developing programs in Python. You will study topics like types, variables, functions, iteration, conditionals, data structures, classes, objects, modules, and I/O operations while also receiving hands-on experience with development environments like Jupyter Notebook and software development practices like test-driven development, debugging, and style. Course projects include real-life applications on enterprise data and document manipulation, web scraping, and data analysis. These courses can be waived for computer science professionals already fluent in Python.

Foundations of Computational Data Science

Course Numbers:  11-671 & 11-672

Learn foundational concepts related to the three core areas of data science: computing systems, analytics, and human-centered data science. In this course, you will acquire skills in solution design (e.g. architecture, framework APIs, cloud computing), analytic algorithms (e.g., classification, clustering, ranking, prediction), interactive analysis (Jupyter Notebook), applications to data science domains (e.g. natural language processing, computer vision), and visualization techniques for data analysis, solution optimization, and performance measurement on real-world tasks.

Course Waivers

Students who already have proficient skills in either math or programming may waive the following courses upon successful completion of an exemption exam(s):

• Math Fundamentals of Machine Learning (10-680) and   Computational Fundamentals of Machine Learning (10-681)
• Python for Data Science (11-604 & 11-605)

The exemption exam(s) will be administered to admitted students only. Students who are interested in taking the exam(s) should indicate their interest in the application when applying to the program. Once admitted, additional information about sitting for the exam(s) will be provided.  

Upon successful completion of one, or both, of the exemption exams, students will only complete the remaining courses to qualify for the certificate. No credit will be earned, nor tuition will be assessed, for the waived courses.  

Please note: Foundations of Computational Data Science is not eligible for a waiver.

For more information about course waivers, contact an admissions counselor today.

Application Deadlines

Priority*: July 9, 2024 Final: July 30, 2024

*All applicants who submit by the priority deadline will receive a partial scholarship award.

Request Info

Questions? There are two ways to contact us. Call 412-501-2686 or send an email to  [email protected] with your inquiries .

Meet Our World-Class Faculty

Dr. carolyn rosé.

Professor of Language Technologies and Human-Computer Interaction

Education:  Ph.D., Carnegie Mellon University

Research Focus: to better understand the social and pragmatic nature of conversation and to build computational systems that improve the efficacy of conversation between people, and between people and computers by using approaches from computational discourse analysis and text mining, conversational agents, and computer-supported collaborative learning. 

Dr. Henry Chai

Assistant Teaching Professor of Machine Learning

Education:  Ph.D., Washington University in St. Louis

Research Focus: topics at the intersection of Bayesian machine learning, probabilistic numerics and active learning that help address the following question: how can we efficiently and accurately reason about inherently intractable quantities? Dr. Chai is also passionate about pedagogical research and K-12 computer science education.

The Graduate Certificate in Machine Learning & Data Science Foundations is offered by the Language Technologies Institute (LTI) at CMU, which is housed within the highly-ranked School of Computer Science (SCS). SCS faculty are esteemed in their field, and many of them have collaborated on critical projects that have paved the way for future discoveries in artificial intelligence. Check out some of their work below:

Researchers from CMU’s Robotics Institute completed a long-distance autonomous driving test in 1995 called the No Hands Across America mission .

In 2001, SCS Founders University Professor Takeo Kanade and his team created a video replay system called EyeVision  for Super Bowl XXXV.

In 2007, Faculty Emeritus William “Red” Whittaker led CMU’s Tartan Racing team to victory in the DARPA’s Grand Challenge .

Assistant Research Professor László Jeni used computer vision technology to create a facial recognition tool  that can help people with visual impairment.

The Building Blocks of Our Curriculum

Industry Impact

In this program, everything you learn serves a greater purpose - to approach and solve large-scale, real-world data challenges in today’s world. After learning fundamental skills in math and computational data science, you’ll have a firm understanding of cloud-based technologies and the ability to solve problems across industries with innovative solutions.

By completing practical, interactive, and collaborative coursework along with hands-on training exercises, you’ll be prepared to: define the analytical requirements of a data science problem, design a data gathering plan, build and deploy models using the right analytic algorithms, and improve models to achieve organizational objectives.

Collaborative

Interdisciplinary work is a core value of Carnegie Mellon. As you complete the coursework for this program, you will explore computational data science from different perspectives, participate in powerful discussions, and gain insights from different departments within the School of Computer Science, including: the Language Technologies Institute, Computer Science Department, Human-Computer Interaction Institute, and Machine Learning Department.

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Bachelor´s program options.

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• Doctor of Business Administration in Managerial Science (DBA)

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Computer and Information Science

School of computer science offers student with detailed training and also provides with the foundation of computing concepts, data storage, networking and other similar concepts related to computer sciences.

Students will also learn the latest development in the field of computing such programming and software development. Students will be introduced to different platforms that are being used all over the world. Students after covering their program will gain skills and knowledge. Students will be able to implement the knowledge into real world situation after completing their program at Holy Spirit University of Atlanta.

Holy Spirit University of Atlanta is renowned for creating leaders who work toward a creating a better tomorrow through the knowledge and skills they have acquired out of their UoA professional studies.

Undergraduate courses in Computer and Information

• Bachelor's Degree

Post Graduate courses in Computer and Information

• Master's Program

Holy Spirit University of Atlanta has a huge global network of students spread across the globe. This has helped the students of Holy Spirit University of Atlanta in acquiring great job opportunities

We believe that your financial constraints should not stop you from pursuing your desired education. That’s why we have carefully developed financial aid plans, scholarships for one, so our education can become easier to access even for those who can’t afford traditional higher studies.

Our Faculty researchers have played a significant role in contributing positively towards the industries development, discoveries and innovative programs which are now being followed all over the globe.

The Faculty of Business & Management offers following Program Majors:

• Master of Science (MS) in Computer Science
• Master of Science (MS) in Computer Science: Management Information Systems (MIS)

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The Holy Spirit University of Atlanta is accredited by the Accreditation Service for International Colleges (ASIC). The University has earned Premier status with ASIC for its commendable areas of operation. ASIC is an approved accrediting body for the purposes of compliance by the UK Border Agency (UKBA) is a member of the British Quality Foundation (BQF), sits on the Quality Standards Group of UK NARIC, and is one of a number of international accrediting bodies listed in the international directory by the Council for Higher Education Accreditation (CHEA) in the USA and is a member of the CHEA International Quality Group (CIQG).

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Do not become a victim. Do not allow criminal activities to flourish at your expense. The UoA takes all such activities serious and will take steps to prosecute the perpetrators to the fullest extent of the law. Stay informed. Stay alert. 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My Progress Points Required Completed points My Progress Progress not available All done! Student hubs Health and counselling All support Health, safety and well-being Breadcrumbs List. About the faculty School of Computer Science You are currently on: Computer Science research page. Open sub navigation overlay. Computer Science Research We have researchers with excellent national and international networks in commerce and academia. Artificial intelligence and machine learning Computational biology, computer networks, internet and mobile computing, computer vision and computer graphics, computing education, cybersecurity, human-computer interaction. Parallel and distributed computing Software engineering Theoretical computer science, collaborations and affiliations. Computer Science flagship projects Doctor of Philosophy Our researchers specialise in creating computational models of living systems and fitting them to real data. This multidisciplinary field of research focuses on the design of computer technology and the interaction between humans (the users) and computers. Our research a range of topics, including the cognitive sciences, philosophy of mind, and biologically inspired AI. Parallel & distributed computing Our research focuses on parallel and distributed algorithms or protocols and their fundamental principles. Computer networking underlies today's mobile devices, home networks and the internet. We address fundamental questions and concrete problems in the communications industry. Computer vision and computer graphics investigates the methods we use to teach computers how to interpret images and video as well as a human. Computers play an increasingly prominent role in education. Our research investigates the ways students learn computing concepts. Cybersecurity focuses on algorithms and systems that protect the security and integrity of computer systems, data and people. Software engineering research seeks to improve the efficiency and quality of software systems. Theoretical computer science focuses on the abstract, mathematical nature of computation. Find a supervisor Search for masters and doctoral supervisors and research projects that you can join. Explore our collaborations with several University-led research centres and organisations. Take 10 with Computer Scienes Discover some of the exciting research happening in the School of Chemical Sciences Jump to section... This dialog can be closed by pressing Escape close button . 379 IMAGES Ioanna SYMEONIDOU Vissarion FISIKOPOULOS Computer Science Summer Camp UoA2017 held｜UoA Computer Science Summer Camp UoA2017 held｜UoA Computer Science and Informatics Computer Science Graduate Program VIDEO Federal board HSSC 2nd annual Result Gazette pdf download 2023 Fbise supply result check by name DIPLOMA PHYSICS CH 1 UNIT AND MEASUREMENTS || PHYSICS || MICROMETER SCREW || PART 3 #ddcet #gtu Stresses in a Plate with a Hole in its Centre_Using ANSYS 30 Competency Based Questions #artificialintelligence #innovation #code #blockchain #robot #programming #electronics #coding #jk gk questions🔥||gk quzi in Hindi||gk quzi||#gk #shots #gkquiz #gkinhindi #brgkstudy #gkfacts#gkfacts COMMENTS Doctoral study in Computer Science Research opportunities. We welcome PhD proposals in areas including: Software Engineering. Systems Development and Evaluation. Theory: Automata theory, computational biology, computational complexity, computability and randomness and the design and analysis of algorithms. Intelligent Systems and Informatics. PhD Studentship in Computer Science at the University of ... The Formal Engineering research group at the School of Computer Science, the University of Auckland, New Zealand is seeking excellent candidates for its growing PhD programme. Founded in 1883, the University of Auckland (UoA) is the country's largest university with a student population of 40,000 includes 7,000 international students ... School of Computer Science At Te Kura Mātai Rorohiko we pride ourselves on our strong teaching abilities and diverse research interests. We are committed to producing first-class graduates at all levels. About the school. Our people. Study Computer Science. Computer Science research. Data Science: Working with industry. Doctor of Philosophy PhD Programme structure. The University of Auckland PhD is a three-to-four year full-time advanced research degree. On entry to the PhD programme, you will be enrolled provisionally. By the end of your first year, you must meet a number of milestones to be confirmed into the PhD programme, including developing a full thesis proposal. Doctor of Philosophy in Computer Science The PhD is a globally recognised postgraduate research degree and the highest level of degree you can achieve. PhD students are critical, curious, creative thinkers who undertake original research over at least 3 years. This course is also offered at overseas locations. Computer Science. Computer Science is the study of computers and computer ... University of Auckland Computer Science PhD Projects ... Integrating data-driven methodologies and model reduction for the control of complex networks. University of Auckland Department of Mathematics. This PhD project will combine data assimilation and model reduction methodologies to predict and control functional failures on complex networks, such as catastrophic blackouts in power grids. Liu AI Lab Senior Lecturer. Email: [email protected]. Bio: Jiamou Liu is a Senior Lecturer at the School of Computer Science, The University of Auckland. He was a Senior Lecturer at Auckland University of Technology between 2011 and 2015. Jiamou obtained a PhD in Computer Science from the University of Auckland, and worked as a research associate ... Bakhadyr KHOUSSAINOV Bakh Khoussainov currently works at the Department of Computer Science, University of Auckland. Bakh's research interests are mostly in Logic, Computability, and Theoretical Computer Science. PhD in Computer Science : Manning College of Information & Computer And work with the brightest minds in the field to advance computing for the common good with UMass Amherst's PhD in computer science. Designed to prepare you for teaching, research and leadership positions, our PhD program ranks among the top 25 in the nation and features world-renowned faculty, interdisciplinary research, and a spirit of ... Apple Scholars in AI/ML 2025 The Apple Scholars in AI/ML PhD fellowship program recognizes the contributions of emerging leaders in computer science and engineering at the graduate and postgraduate level. The PhD fellowship in AI/ML was created as part of the Apple Scholars program to support the work of outstanding PhD students from around the world, who are pursuing cutting edge research in machine learning and ... Machine Learning Group at the University of Auckland University of Auckland Machine Learning Group. Machine Learning is a field of Computer Science that aims to develop algorithms that learn from experience. This is in contrast to traditional computer science algorithms where behavior is directly coded into the algorithms. Machine Learning algorithms take experience in form of data and observe ... Thomas LACOMBE University of Auckland · Department of Computer Science. PhD. Contact. Connect with experts in your field. UoA Computer Science PhD FB page Facebook page for UoA CS PhD students. Feel free to engage in general banter, advertise talks/events/post random (but relevant!) memes, and what not. UoA Computer Science PhD FB page Postgraduate Computer Science The Department of Computer Science offers subjects mostly within the two general areas of software systems and theory of computing. The department has strong research activities, clustered in several substantial research groups, in algorithmic information theory, artificial intelligence, combinatorics, data compression, data communications and networks, distributed computing, graphics, image ... Graduate Admissions The College of Information Science (InfoSci) offers leading graduate degrees in Data Science, Information Science, and Library and Information Science that prepare you for a wide range of in-demand careers across disciplines and industries.. We are dedicated to your success, delivering a world-class, cross-disciplinary education that empowers you with the skills and knowledge you need to ... Electrical Engineering Graduate Programs You can add to our legacy in world-class photonics, high-frequency diodes, and materials science research, and extend our long-standing tradition in computer engineering, devices and circuits, signal and image processing, and communications. GATE 2025 The test papers will be in English. Each GATE 2025 paper is for a total of 100 marks, General Aptitude (GA) is common for all papers (15 marks), and the rest of the paper covers the respective test paper syllabus (85 marks). Click here for detailed pattern of the question papers. Robert AMOR Robert AMOR, Professor | Cited by 2,952 | of University of Auckland, Auckland | Read 238 publications | Contact Robert AMOR hUMNs of Chemistry #17 Lee PennThey/themProfessorTell us about your journey to the University of Minnesota.My first visit to the University of Minnesota was during my graduate school years at UW-Madison. I came to make measurements at the Institute for Rock Magnetism and also ended up meeting the love of my life during that visit. Fast forward, the love of my life and I live right here in Minneapolis. We would love ... CMU's Online Graduate Certificate in Machine Learning and Data Science At a Glance. Start Date August 2024. Application Deadlines Priority*: July 9, 2024 Final: July 30, 2024 *All applicants who submit by the priority deadline will receive a partial scholarship award. Program Length 12 months. Program Format 100% online. Live-Online Schedule 1x per week for 90 minutes in the evening. Taught By School of Computer Science. Request Info Computer Science Computer technology, it seems, is everywhere in today's world. It has become an ever-growing part of human life, affecting many aspects of a person's day. Computer scientists have an impact on how our society advances by developing and maintaining these systems: whether it be for our home, work, learning or entertainment environments. We ... How has your experience of a comp sci degree in UoA been? Reply. Fantastic-Stage-7618. •. Too much risk competing for software and potentially wasting a year imo. If you want to do a 4 year degree you can do compsci honours. Reply. MathmoKiwi. •. UoA will give you a more rigorous and deeper degree that will stand the test of time than AUT would. Postgraduate study in Computer Science 60 points from: COMPSCI 701 Special Topic. COMPSCI 702 Security for Smart Devices. COMPSCI 703 or 704 Special Topic. COMPSCI 705 Advanced Topics in Human-computer Interaction. COMPSCI 706-708 Special Topic. COMPSCI 709 Directed Study. COMPSCI 710 Directed Study. COMPSCI 711 Parallel and Distributed Computing. Robert AMOR University of Auckland · Department of Computer Science. PhD Computer Science. Contact. Connect with experts in your field. The University of Auckland COMPSCI 316: Cyber Security. Semester 2, City Campus. The course introduces various concepts related to software, system, and network security. It covers a range of topics including attacks on privacy and attack surface, static and dynamic analysis of malware, hardware security, network security and some hot topics including blockchain and bitcoin. Apply to CMU's Online Graduate Certificate in Machine Learning and Data The Graduate Certificate in Machine Learning & Data Science Foundations provides a unique opportunity for individuals nearly everywhere to earn a certificate at the intersection of AI, machine learning, and computational data science from one of the top ranked computer science schools in the country. Home Learn more about the outstanding work being done by our graduate students. Graduate Research. Department Job Opportunities. Currently open tenure-track faculty, career track faculty, and staff positions. ... Dr. Diana Diazh joining UA Department of Computer Science! May 29, 2024. Read more Image. Portia Cooper receives 2024-2025 Barry Goldwater ... CMU's Cutting-Edge Curriculum Course Numbers: 11-604 & 11-605 Units: 6 units each Master the concepts, techniques, skills, and tools needed for developing programs in Python. You will study topics like types, variables, functions, iteration, conditionals, data structures, classes, objects, modules, and I/O operations while also receiving hands-on experience with development environments like Jupyter Notebook and software ... School of Computer and Information Science School of computer science offers student with detailed training and also provides with the foundation of computing concepts related to computer sciences. ... UoA´ 100% Online degree programs allow you to reach your goals on your terms. Choose a program that suits your career read. ... Post Graduate courses in Computer and Information. Master ... Computer Science research Theoretical computer science. Theoretical computer science focuses on the abstract, mathematical nature of computation. We have many research-active staff with excellent national and international links, both to commerce and academia. assignment essay homework paper phd report resume review speech thesis