Machine Learning (ML) has made significant progress in recent years, powered by the development of new algorithms, availability of data, open-source software, and ever-increasing computational resources.
However, most applications in which machine learning has been successful so far are limited to the cases where data is abundant and cheap to gather, such as images, advertising, website user interactions, and financial data. In contrast, in many real-world scientific applications, data is scarce and expensive to collect and label, which mandates the use of historic data. Unfortunately, such data is often not representative of novel data points on which we want to perform predictions, a problem more formally called domain shift. This is even exacerbated when models trained on historic data are used in conjunction with autonomous decision-making agents.
In this project, you will work towards addressing these limitations of machine learning. The goal is to develop improved algorithms which are able to better extract relevant information from data instead of spurious correlations, are robust to domain shift and generalise better to novel situations, and can therefore be employed within decision making agents. To assess how the methods perform, you can address real-world examples from important chemistry problems, such as drug and materials discovery.
Position and host institutions
The student will be primarily based at the UCL AI Centre in the department of Computer Science, based at 90 High Holborn, London, but will spend at least three months of the PhD at Microsoft Research in Cambridge.
Candidate
Applications are invited for a funded 4-year PhD studentship at the UCL Department of Computer Science, starting either September 2021 or January 2022 (flexible), under the supervision of Dr Brooks Paige (UCL AI Centre) and Dr Marwin Segler (Microsoft Research Cambridge). Applicants should have:
● A BSc or MSc degree in Machine Learning, Mathematics, Computer Science, Statistics, Physics, Chemistry or related fields
● Experience with programming, numerical simulations, machine learning, and/or scientific computing
● Strong mathematical background
● Good communication skills, especially in written English
● Ability to work and think creatively both independently and in a team
● Strong interest in interdisciplinary work
● (Ideally) Previous research experience at the undergraduate or masters level
How to apply
Interested candidates are encouraged to contact both supervisors before submitting an application, via email to Dr. Brooks Paige ([Email Address Removed]), with a CV and a short 1-paragraph description of research background and interests. A complete formal application must be submitted via UCL Select no later than July 30th, 2021, stating Dr. Brooks Paige as intended supervisor and stating this studentship. Detailed information about the PhD programme and how to apply can be found at:
https://www.ucl.ac.uk/prospective-students/graduate/research-degrees/computer-science-mphil-phd
Funding and eligibility
This studentship is funded by the Engineering and Physical Sciences Research Council (EPSRC) i-CASE programme and Microsoft Research, Cambridge. Funding will be for 4 years, with a tax-free stipend of approximately £22,300 in the first year (rising in subsequent years), plus home-level university fees, with additional funding to cover travel and computing equipment. Eligible candidates are: UK nationals that meet residency requirements; EU nationals with settled status; EU nationals with pre-settled status that meet residency requirements; Irish nationals living in the UK or Ireland; those who have indefinite leave to remain or enter.
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