PhD in Machine Learning
About This Programme
A University-funded (Year 1, pending a faculty advisor match) PhD in Machine Learning: 60 credits over 3–4 years with a 32-credit research thesis.
Course Highlights
- Ph.D. students are centrally funded by MBZUAI in Year 1 while matching with a faculty advisor, then funded by that advisor from Year 2, subject to a confirmed match and satisfactory progress.
- Research spans optimization, causality, RL, interpretability, privacy/fairness and generative AI.
- Access to computing labs with nearly 1,000 Nvidia DGZ-1/2 GPU servers.
- Qualifying and Candidacy exams then a 32-credit thesis.
What You'll Study
A 3–4 year full-time doctoral programme on the Masdar City campus: core courses, electives, advanced research methods and an internship in the early years, a Qualifying Exam at the end of Year 1 and a Candidacy Exam at the end of Year 2, then an original 32-credit research thesis. Ph.D. students are centrally funded by MBZUAI in Year 1 while matching with a faculty advisor, then move to that advisor's funding from Year 2, subject to a confirmed match and satisfactory progress.
Core courses
Core: 3 courses, 8 credit hours, taken by every student.
- Foundations of Machine Learning (ML8101)2 credits
- Advanced Machine Learning (ML8102)2 credits
- Causality and Machine Learning (ML808) OR Sequential Decision Making (ML8103)4 credits
Research methods, internship and thesis
- Advanced Research Methods (RES899)2 credits
- Ph.D. Internship (up to four months) (INT899)2 credits
- Machine Learning Ph.D. Research Thesis (ML899)32 credits
Specialised electives (at least 8 credit hours)
Students must select at least eight credit hours from the ML specialised electives.
Published elective list
- Advanced Topics in Continuous Optimization (ML804)4 credits
- Causality and Machine Learning (ML808)4 credits
- Sequential Decision Making (ML8103)4 credits
- Tiny Machine Learning (ML8505)2 credits
- Interpretable AI (ML8506)2 credits
- Predictive Statistical Inference and Uncertainty Quantification (ML8507)2 credits
- Privacy and Fairness (ML8508)2 credits
- Collaborative Learning (ML8509)4 credits
- Graph Machine Learning and Topics in Generative AI (ML8510)2 credits
- Machine Learning for Industry (ML8511)2 credits
Further electives (remaining 8 credit hours)
The remaining eight credit hours are selected from the wider doctoral list.
Published elective list
- Single Cell Biology and Bioinformatics (CBIO803)4 credits
- Advanced Computer Vision (CV801)4 credits
- Advanced 3D Computer Vision (CV802)4 credits
- Advanced Techniques in Visual Object Recognition and Detection (CV803)4 credits
- Lifelong Learning Agents for Vision (CV805)4 credits
- Advanced Topics in Vision and Language (CV806)4 credits
- Safe and Robust Computer Vision (CV807)4 credits
- Trustworthy Medical Vision (CV8502)2 credits
- Advanced Topics in Large Multimodal Models (CV8503)2 credits
- Advanced Topics in Reinforcement Learning (ML806)4 credits
- Causality and Machine Learning (ML808)4 credits
- Advanced Learning Theory (ML809)4 credits
- Dimensionality Reduction and Manifold Learning (ML813)4 credits
- Selected Topics in Machine Learning (ML814)4 credits
- Advanced Parallel and Distributed Machine Learning Systems (ML815)4 credits
- AI for Science and Engineering (ML817)4 credits
- Emerging Topics in Trustworthy ML (ML818)4 credits
- Sequential Decision Making (ML8103)2 credits
- Algorithms for Big Data (ML8501)2 credits
- Machine Learning Security (ML8502)4 credits
- Probabilistic Graphical Models (ML8503)2 credits
- Data Synthesis (ML8504)2 credits
- Tiny Machine Learning (ML8505)2 credits
- Interpretable AI (ML8506)2 credits
- Predictive Statistical Inference and Uncertainty Quantification (ML8507)2 credits
- Privacy and Fairness (ML8508)2 credits
- Collaborative Learning (ML8509)4 credits
- Graph Machine Learning and Topics in Generative AI (ML8510)2 credits
- Machine Learning for Industry (ML8511)2 credits
- Natural Language Processing - Ph.D. (NLP805)4 credits
- Advanced Natural Language Processing - Ph.D. (NLP806)4 credits
- Speech Processing - Ph.D. (NLP807)4 credits
- Current Topics in Natural Language Processing (NLP808)4 credits
- Advanced Speech Processing (NLP809)4 credits
- Vision-to-Language Generation (NLP8501)2 credits
Entry Requirements
Three references and an independent research statement (written unaided). Screening exam and technical interview.
Careers
Graduates pursue careers as research scientists and faculty in academia, national labs and industry AI research.
Accreditation & Professional Recognition
Accredited by the UAE Commission for Academic Accreditation (status Active on the CAA register).
Programme Details
Award
Doctor of Philosophy (PhD) in Machine Learning
Start Date
Annual Fall intake (August)
Typical Offer
Bachelor's CGPA 3.5 or master's 3.0 (STEM); IELTS 6.5 / TOEFL 90
Duration
4 years
Qualification
Doctorate
Subject Area
Machine Learning
Study Pattern
Full time
Delivery Format
In person