Fixed-term: The funds for this post are available for 3 years in the first instance.
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.
For advice about potential supervisors and the application process, including advice on which department to apply to, please contact Jess Montgomery (email@example.com).
In making an application, candidates should clearly state - in their research topic and in the funding section - that they wish to be considered for the Accelerate Programme. Admissions to the graduate program and competition for the studentship will be considered separately.
Applicants are encouraged to make informal contact with potential supervisors prior to making an application. Applications must be made via University Application Portal www.postgraduate.study.cam.ac.uk/application-process/applicant-portal-and-self-service-account from which candidates can select the Department or Institute to which they wish to apply. Applicants are advised to consult the University Graduate Admissions Website www.postgraduate.study.cam.ac.uk/application-process/how-do-i-apply for details of the admissions process. Applications close on 3 December 2020.
Please quote reference NR24589 Accelerate Programme on your application and in any correspondence about this vacancy.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.