Senior Research Associates in AI for Scientific Discovery (PolymathicAI) (Fixed Term)
Join to apply for the Senior Research Associates in AI for Scientific Discovery (PolymathicAI) (Fixed Term) role at Cambridge Service Alliance
Senior Research Associates in AI for Scientific Discovery (PolymathicAI) (Fixed Term)
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Join to apply for the Senior Research Associates in AI for Scientific Discovery (PolymathicAI) (Fixed Term) role at Cambridge Service Alliance
We invite applications for up to two Senior Research Associate (Grade 9) posts to join PolymathicAI in the Department of Applied Mathematics and Theoretical Physics (DAMTP). PolymathicAI is an international collaboration that researches and develops industry-scale foundation models for scientific data, using large datasets curated by domain experts across the physical sciences. We collaborate locally with the GK Batchelor Laboratory, the Infosys-Cambridge AI Centre, and the Institute of Computing for Climate Science, and have many collaborators internationally, particularly at the Flatiron Institute. Projects span turbulence and fluid dynamics, and mechanistic interpretability for physics discovery. Candidates may indicate a preference for one post or be considered for both.
Post 1: Model discovery in experimental fluids data (with GK Batchelor Lab): Help shape model discovery for experimental fluid dynamics as a core member of PolymathicAI. You'll help set how experimental data shapes next-generation foundation models for fluids. Working with DAMTP's GK Batchelor Laboratory, you'll integrate world-class, uniquely rich measurements into large-scale training datasets, co-design and train foundation models for fluids, and co-lead methods that fuse experiments with simulations to answer new physics questions. There is scope to propose and develop complementary directions aligned with the project's goals.
Post 2: Mechanistic interpretability for scientific foundation models: Investigate what scientific foundation models learn, and help shape how we build them in PolymathicAI, working across our multidisciplinary foundation models trained on datasets in fluid dynamics, astronomy, chemistry, and other domains. Identify and test latent mechanisms, assess out-of-distribution reliability, and distil behaviour into interpretable forms, including symbolic models. Compare and transfer mechanisms across models to reveal shared structure, feeding results back into data and model design. There is scope to propose and develop complementary directions aligned with the project's goals.
As PolymathicAI member, both posts will contribute to designing and executing large-scale training experiments on GPU/HPC clusters (allocations vary; previous campaigns used up to ~1,400 H100 GPUs) and use the resulting models to generate new scientific insight.
Key responsibilities: Develop and deliver individual and collaborative research objectives, proposals and projects. Build, maintain and release open-source research software; manage and curate large scientific datasets for model training and evaluation. Design and run experiments on GPU/HPC systems; analyse and communicate results; prepare publications and presentations. Engage with collaborators across Cambridge and internationally; mentor students and junior researchers; contribute to teaching where appropriate. Before applying, please read the Further Particulars attached for details of essential/desirable criteria.
Limit of tenure: 3 years, subject to funding.
Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.
Please upload your full CV; and either a brief statement (max 1 page) describing fit and recent work or a short cover note in the form. Please provide contact details for two referees; references will be requested at shortlisting.
Informal enquiries: Assistant Professor Miles Cranmer, mc2473@cam.ac.uk. Process queries: LE46137@maths.cam.ac.uk. Online interview dates to be confirmed.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society. We particularly welcome applications from women and/or candidates from a BAME background for this vacancy as they're currently under-represented at this level in our Department. The University has a responsibility to ensure all employees are eligible to live and work in the UK.
Apply online
Seniority level
* Seniority level
Mid-Senior level
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Full-time
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Research, Analyst, and Information Technology
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Education Management
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