AI in Science
I-X Centre for AI in Science
We are delighted to announce Imperial’s I-X Centre for AI in Science. I-X is Imperial’s flagship AI initiative bringing AI together with interdisciplinarity and grand challenges. Our centre is dedicated to using AI to disrupt and advance Science, Engineering and Mathematics and is underpinned by core support from Schmidt Futures.
Our centre will host over 130 years of time of Eric and Wendy Schmidt AI in Science Postdoctoral Fellows, a program of Schmidt Futures and starts by advertising 30 roles. The overall support will extend over the next 6 years. These prestigious fellows will be given bespoke training and 1-on-1 career development support and join a collocated cohort of top scholars based in I-X. The fellowships are flexible and independent, allowing recipients to freely explore while drawing on expert faculty mentors of their choice. These fellowships have the aim of identifying excellent researchers and accelerating them in using AI to advance and disrupt Science or Engineering. Our fellows will benefit from an Advanced Courses in AI Hub, pre-acceleration training material, appointment-specific faculty interview preparation and grant writing courses. The program is also supported by Imperial’s Chapman Fellowships and Imperial College Research Fellows and partners at AIMS with close partners including the CNRS and ICTP.
We believe that some of the most critical discoveries in the future will be as a result of the application and development of AI techniques to Engineering, Natural and Mathematical Sciences. Our planned and ongoing research extends from developing new generative language models for mathematical proof assistants, through to enhanced microscopy techniques.
We will position our Centre to exploit both the possibilities offered by modern AI, and to speed the evolution of new types of AI solving the well-defined challenges of particular areas of Science and Engineering.
Imperial College was recently (May 2022) recognized in the government’s country-wide Research Excellence Framework (REF) as having a greater proportion of 4* “world-leading” research than any other university in the country. Imperial also ranked first in the UK for research outputs, first in Engineering and Computing, first in the UK for research environment, and first for research impact amongst research-intensive universities. AI is at the heart of Imperial College’s 2020-25 academic strategy and is a key cross-cutting research theme. As a result, Imperial has committed a substantial investment to a flagship new interdisciplinary AI initiative called I-X. I-X builds on the expertise of >300 faculty across the university. As a new cross-departmental co-location campus themed on AI, I-X brings together Imperial’s strengths in both foundational and applied AI with our great interdisciplinary challenges: this trio of AI, interdisciplinarity and grand scientific challenges is core to our Schmidt Futures supported Centre.
We believe that in receiving support for talented individuals our Fellows are privileged and with this comes a responsibility to create opportunities for others. We will foster a culture of respect and empathy for others and purposeful happiness. Kindness among scientists will be a key theme in our centre. We will create opportunities for, and work with. the local community and researchers and students from Lower and Middle Income Countries.
We are hiring:
We have openings for:
- 10 1-year Eric and Wendy Schmidt AI in Science Postdoctoral Fellowships to start in early 2023.
- Up to 20 1 or 2-year Eric and Wendy Schmidt AI in Science Postdoctoral Fellowships to start in September 2023.
- 2 2-year Chapman-Schmidt AI in Science Postdoctoral Fellowships (Mathematics) to start in September 2023.
- 1 Centre Manager (level 4) to start in the next few months.
- 1 Senior Teaching Fellow (6 years) advertised shortly, closing mid-January, details here.
- 1 Postdoc and Fellows Development Centre Expert to start in the next few months.
- Enquiries to email@example.com
- Hyperlinks to the advertisements will be provided above as they are posted in the next few days.
The majority of the fellowships will be of 2 year duration but some will be 1 year. Please indicate whether you would consider a 1 year appointment.
Brief details of the fellowship applications are given below.
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 very 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 seeking to tackle particular pieces of Science or Science researchers using AI to transform their area. The fellows will be collocated within Imperial’s dynamic new AI campus, I-X, and alongside other AI researchers from across Imperial’s faculties and departments.
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. This should be suitable for a general/lay reader.
- Research Proposal Lay Summary: a 1 page or less, summary of the proposed research suitable for a general/lay 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 mentioning, the applicant’s proposed department and at least one mentor who could support the application – one mentor should be within the fellow’s proposed 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. The bulk of the fellowships will be of 2 year duration but some may be 1 year. Please indicate whether you would consider a 1 year appointment.
- Research Proposal: [not required for the 1-year fellowships] 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 Lay 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.
- 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.
Please note that applications that focus on AI alone, or focus on AI with broad application to numerous example areas, will not be considered: a focus on advancing new science in a particular area is essential. The use of AI must serve a catalytic role: applications which use AI in a manner judged routine across the specific field (e.g. this might apply to some parts of bioinformatics) maybe be at a disadvantage. With exceptions like AI for Mathematical Proofs, publications/ outputs in mainstream venues read by a broad range of scientists/ engineers, will typically be expected.