Machine Learning PhD Position
- Electronic and Electrical Engineering department, University College London
- Start Date: 2022
- Duration: A four year fully-funded
For inquiries about the position, please first reach out at email@example.com
The new machine learning research group led by Dr Ilija Bogunovic at the Electronic and Electrical Engineering department (at UCL) is looking for motivated PhD students to work in the areas of machine learning and AI. The student will work on algorithms, real-world applications, and/or theory of modern interactive learning systems, with research topics potentially including the following:
- Learning and sequential decision making under uncertainty (e.g., reinforcement learning, Bayesian optimization, bandits and exploration),
- Reliability, robustness, and sample-efficiency considerations in algorithmic data-driven decision making,
- Multi-agent reinforcement learning (MARL) and games,
- Large-scale real-world experiment design and active learning.
For more information on my research, please see my publications.
What does the PhD project entail? The students will conduct research in the field of machine learning with the emphasis on modern algorithms and applications; write research papers and present results at leading international ML conferences, e.g., NeurIPS, ICML, ICLR, AISTATS, UAI, etc.; collaborate with internal and international teams of research scientists.
How to apply:
Applications must be made using the UCL online application system
by using the UCL postgraduate study application form. Please submit your CV, transcripts (both undergraduate and postgraduate), your latest thesis (and/or one of your publications if applicable), and a short (up to one page) cover letter explaining why you think you are a suitable candidate for this post. The short-listed candidates might be further required to provide contact info (no direct recommendation letters) for peers that can recommend them.
The candidate should meet the entry requirements for PhD programmes at UCL EEE. Additional requirements include an outstanding academic record and strong mathematical background. Experience with research and coding is a plus. Also desirable: prior knowledge in machine learning, AI, optimization, statistics.