FURTHER INFORMATION

In addition to completing the online application, candidates should, after carefully consulting the Job Description, FAQ and related documents, upload the following documents:

• A full CV including publications.

An additional single file with:

• Publication Elaboration: a 1 page, or less, note outlining the contribution of up to three papers by the applicant suitable for a general scientific reader.
• Research Proposal Summary: a 1 page or less, summary of the proposed research suitable for a general scientific audience. Particular attention will be paid to this summary. It should answer the question of why/how this application of AI will be transformative for the target area of science. The proposal should start by confirming the primary mentor who supports the application – the primary mentor MUST be within the Maths Department, but others could be outside. Mentors must be contacted in advance of the application. It is not essential that the mentor be a very close fit to the proposed research, entirely independent research efforts are welcomed, but a collaborative mentorship is likely to make the science more credible.
• Research Proposal: a 3 pages or less proposal that explains why and how the proposed research could be transformative for a particular area of science. It can be structured around background, a small number of hypotheses/aims, and work packages. It can be assumed that the reader will first read the Summary and so content need not be repeated.
• Training Plan: a ½ page or less plan, identifying any particular skills that need to be acquired for the proposed research to succeed. Training is a key part of the proposed fellowship, whether helping an AI expert master a scientific topic or a scientific topic expert advance their AI skills. Deep expertise in AI (or the particular Science area) is not a pre-requisite: the minimum level of AI/Science experience is that needed to credibly articulate a plan for how AI will advance Science.
• Fit to AI in Science Remit: a ¼ page or less outline of how your proposal fits within the AI in Science remit – please check your proposal’s fit here.
• Kindness Statement: a ¼ page or less outline of your view on the need for kindness among scientists. The fellows will join a cohort of fellows in AI for Science with opportunities for outreach and LMIC engagement.
• Mobility Statement (for applicants currently at Imperial only): Schmidt Sciences seeks to enable national and international mobility: candidates from outside Imperial are particularly encouraged to apply. Candidates that are already in Imperial are asked to supply several sentences in their mobility statement outlining, in a distinctive and compelling manner, how remaining in Imperial is the best route for their personal growth. Applicants from Imperial are required to move from their existing supervisor to entirely new mentors for their Chapman Schmidt Fellowship, to increase both their independence and their breadth of experience (existing mentors cannot become secondary mentors).

AVAILABLE DOCUMENTS

Attached documents are available under links. Clicking a document link will initialize its download.
Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.
We reserve the right to close the advert prior to the closing date stated should we receive a high volume of applications. It is therefore advisable that you submit your application as early as possible to avoid disappointment.
If you encounter any technical issues while applying online, please don’t hesitate to email us at support.jobs@imperial.ac.uk. We’re here to help.

ABOUT THE ROLE

Applications are invited for prestigious Chapman Schmidt AI in Science Fellowships, a program of Schmidt Sciences, commencing 1 September 2026. There are 2 positions available, with a duration of 2 years.

WHAT YOU WOULD BE DOING

The Chapman Schmidt AI in Science Fellows will produce independent and original research, using AI to advance science, within the Maths Department and the I-X Centre for AI in Science. These fellowships have the aim of identifying excellent researchers and accelerating them in using AI to advance and disrupt Science or Engineering. Here ‘AI’ is interpreted very broadly, e.g.: topics in Bayesian Inference and Robotics; ‘Science’ covers any typical topic in Natural Science and Engineering (Epidemiology, Biology and basic science in biomedicine are included but clinical medical themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and astrostatistics. These posts are not suitable for generic AI research with general application: candidates must be aiming to substantially advance a particular area of science. Applicants could view themselves as AI researchers tackling particular pieces of science or science researchers using AI to transform their area. Extensive AI knowledge is not required, and AI training is offered.