Department of Computer Science and Technology, West Cambridge

The Accelerate Programme for Scientific Discovery is a high-profile Cambridge University initiative promoting the use of machine learning to tackle major scientific challenges. The Programme is building a community of committed researchers working at the interface of machine learning and different scientific domains, with the aim of accelerating the pace of scientific discovery.

We are inviting applications for a three-year doctoral studentship with the Accelerate Programme. Successful applicants will develop a project at the intersection of machine learning and a scientific discipline, pursuing research that contributes to both the advancement of that discipline and progress in machine learning. Current areas of interest for the Programme include the use machine learning to advance research in:

• Applied mathematics and theoretical physics: how can we use machine learning to better understand complex geometries and create new understandings of space-time or quantum gravity?
• Genomics and computational biology: how can machine learning-enabled advances in genomics help us better understand the building blocks of living systems (and how they contribute to individual health and wellbeing)?
• Physical sciences: how can machine learning help us understand interactions between atoms, and design new materials?
• Psychiatry: how can researchers and clinicians use machine learning tools to better understand and predict mental health conditions?

Applicants are invited to propose topics that would advance research in one of these themes or that bridge these areas of research.

In developing project ideas, applicants may wish to seek inspiration from the work of research leaders connected to the Programme:

• Bingqing Cheng, Accelerate Science Research Fellow, Department of Computer Science and Technology https://sites.google.com/site/tonicbq/
• Bianca Dumitrascu, Accelerate Science Research Fellow, Department of Computer Science and Technology https://b2du.github.io/
• Carl-Henrik Ek, Senior Lecturer in Machine Learning, Department of Computer Science and Technology http://carlhenrik.com/
• Austen Lamacraft, Professor of Theoretical Physics, Department of Physics https://auste.nl/
• Neil Lawrence, DeepMind Professor of Machine Learning, Department of Computer Science and Technology www.csap.cam.ac.uk/network/neil-lawrence/
• Challenger Mishra, Accelerate Science Research Fellow, Department of Computer Science and Technology https://oatml.cs.ox.ac.uk/members/challenger_mishra/
• Sarah Morgan, Accelerate Science Research Fellow, Department of Computer Science and Technology https://semorgan.org/
• Carola-Bibiane Schönlieb, Professor of Applied Mathematics, Department of Applied Mathematics and Theoretical Physics www.damtp.cam.ac.uk/research/cia/
• Sarah Teichmann, Director of Research, Department of Physics, and Head of Cellular Genetics, Wellcome Sanger Institute http://www.teichlab.org/

The successful candidate will have a strong interest in working at the interface of machine learning and the sciences. They will require excellent oral and written communication skills; good team-working skills, and strong motivation for the project. Applicants will be expected to have a 1st or 2.1 degree in a related subject and hold (or be studying for) a master's degree in a relevant specialist area.

Fixed-term: The funds for this post are available for 3 years in the first instance.

Full details: http://www.jobs.cam.ac.uk/job/27517/