In October 2022 the University of Oxford became one of nine leading research universities around the world selected to deliver the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship programme. Ten Fellows were recruited to Oxford in 2023 and this year a further 15 Fellows are joining them, including two in the Department of Chemistry.
The Schmidt AI in Science Postdoctoral Fellowship, a programme of Schmidt Sciences, aims to accelerate the next scientific revolution by supporting talented postdoctoral researchers to apply AI techniques across the natural sciences, engineering and mathematical sciences. This initiative adds to Schmidt Sciences’ existing philanthropic efforts to support the development and application of AI in innovative ways.
The programme is cross-disciplinary and spans the full breadth of MPLS Division, which gives it the ability to bring together different parts of the AI landscape in novel ways. Another key element of the AI in Science programme is the training aspect, providing Fellows with all they need as non-AI specialists to use AI in their respective research fields. In addition, the Eric and Wendy Schmidt AI in Science Postdoctoral Fellows have the opportunity of becoming Associate Research Fellows at the University’s newest College: Reuben College. Founded in 2019, Reuben College aspires to create a community of scholars embracing opportunities for interdisciplinary collaboration, and developing initiatives to generate wider impacts and positive benefits from research, entrepreneurship and public engagement.
The projects of the new Schmidt AI in Science Fellows in the Department of Chemisty are:
Veronika Juraskova (Duarte Group): Different flavours of machine learning potentials for modelling processes in complex solvated environments
Solvents are integral components of chemical processes. Most commonly used solvents are toxic to humans and sources of pollution and ideally need replacing with environmentally friendly alternatives. Computational modelling offers insights into chemical reactions at the molecular level and may guide the design of novel synthetic paths. However, modelling chemical reactions in sustainable solvents remains challenging and computationally demanding. This research aims to develop machine learning-based potentials, offering a fast and accurate approach to modelling chemical reactions in complex solutions.
Jan Christoph Thiele (Kukura Group): From noise to knowledge: deep learning for weak signal analysis in ultra-sensitive microscopy and beyond
In biochemical and pharmaceutical research, understanding interactions between proteins is of paramount importance. Mass Photometry (MP) has emerged as a promising microscopy technique for studying these interactions at the single-molecule level, but faces some limitations. The project aims to expand MP's applicability, building on existing deep learning approaches, including leading methods for super-resolution microscopy and specialised AI tools.
Professor Jim Naismith, Head of Division for the Mathematical, Physical and Life Sciences, said:
Oxford stands at the forefront of AI research, and witnessing the transformative impact of the Schmidt AI in Science program on researchers within the MPLS Division is truly inspiring. It's remarkable to see how AI is envisioned to address a vast array of challenges, igniting imaginations and propelling innovation forward. We extend our heartfelt gratitude to Schmidt Sciences for their invaluable support and vision in making this pioneering program possible.
You can read more about all the new fellows on the MPLS website.
