| Location: | Cambridge |
|---|---|
| Salary: | £37,694 to £59,966 per annum |
| Hours: | Full Time |
| Contract Type: | Fixed-Term/Contract |
| Placed On: | 11th September 2026 |
|---|---|
| Closes: | 25th September 2026 |
| Job Ref: | LG51052 |
Fixed-term: The funds for this post are available until 30 September 2030 in the first instance.
Applications are invited for a circa four-year research position in AI for fundamental physics and cosmology in the Handley Lab at the Kavli Institute for Cosmology, Institute of Astronomy, University of Cambridge. Appointment will be at Research Associate level (Grade 7) or Assistant Research Professor level (Grade 9), according to the successful candidate's skills, experience and research profile. Please see the Further Particulars associated with this vacancy for more information on what is required of both the Grade 7 and the Grade 9 role.
Please note that appointment at Grade 9 is subject to application and approval to the Faculty Board. If any Faculty Board application is unsuccessful then appointment will be made at Grade 7 Research Associate.
The conventional account of fundamental physics begins with a Lagrangian, derives its observable consequences and ends with a comparison against data. In modern cosmology, the middle of this process has become the difficult part. New theories require substantial calculations, specialised numerical methods and research software capable of carrying their predictions through to cosmological observables. Much of this machinery is concentrated in large collaborations and established frameworks, while tractability at cosmological scales often relies on effective descriptions which have integrated out the physics under investigation.
This creates a bias in which theories are tested. So long as it is substantially harder to investigate a new theory from scratch than to rerun an established model, our attention will be directed towards the theories which are easiest to implement rather than those which are most scientifically promising.
The project will use AI-assisted development and GPU-accelerated inference to change what can be attempted by a small research group. The postholder will derive physical predictions, direct the construction of the software needed to test them, and confront the resulting models with cosmological and astrophysical data. The scientific judgement remains with the researcher: deciding which problems are worth pursuing, understanding what the calculations mean, and recognising when the machinery is wrong.
The position offers a larger than usual amount of research freedom. Candidates will be encouraged to shape the programme around their own interests and may come from any relevant area of theoretical or computational physics, including gravitation, field theory, lattice and numerical field theory, cosmological perturbation theory, Bayesian computation, GPU and differentiable programming, or AI-assisted scientific software development.
The postholder will also develop GPU-accelerated Bayesian inference methods, including nested sampling, publish and present their research, and contribute to the fundamental physics, cosmology and astronomy communities in Cambridge.
Applicants should have, or be close to obtaining, a PhD in physics, astronomy, applied mathematics or a closely related field. Theoretical capability is the essential criterion. Fluency in directing AI agents matters more than prior strength as a programmer, provided the candidate has the judgement to assess their outputs and correct the implementation.
To apply online for this vacancy and to view further information about the role, please click the 'Apply' button above.
If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.
Informal enquiries are welcomed and should be directed to: Dr Will Handley
Email: wh260@cam.ac.uk
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
Type / Role:
Subject Area(s):
Location(s):