Research Fellow Artificial Intelligence for Materials Discovery (AIChemy) Grade 7

A Research Fellow position is available in the group of Professor M. J. Rosseinsky OBE FRS to work in a team of computer scientists and materials chemists funded by the AlChemy. AI in Chemistry Hub: AlChemy is a new partnership between the University of Liverpool, Imperial College London and a large consortium of academic and industrial partners.

AIChemy has been funded to promote the development of novel AI, alongside the increased uptake of AI amongst the chemistry community. The consortium brings together experts across AI and both experimental and computational chemistry and this Hub will promote connectivity of the broader community, training, networking, as well as state-of-the-art research.

This post will develop artificial intelligence methods for the prediction of crystal structure, the critical step in materials prediction for discovery. It will develop and apply new ML approaches to crystal structure prediction.

Based at the University of Liverpool, you will have a key role in one of the forerunner projects of AlChemy, namely “Human in the Loop”, which aims at integrating cutting-edge AI technologies with Robotics in accelerating the discovery and synthesis of new materials. You should have a PhD in a relevant field (Computer Science, Mathematics are most likely to fit the role but we are open to Chemistry, Materials Science, Chemical Engineering, etc.), expertise in cutting-edge AI and machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role.

This post would be ideal for an ambitious and innovative scientist who is driven, enjoys working in a diverse team, keen to share knowledge and eager to train others in the group. In this project, engagement with chemists and materials scientists is essential to ensure that the developed methods make optimal use of domain expertise and integrate fully into “human in the loop” workflows.

This post is available for two years.

Keywords: Geometric Deep Learning, in particular Graph Neural Networks, Deep Reinforcement Learning, Generative Modelling, in particular Denoising Diffusions, Combinatorial Optimisation

Commitment to Diversity

The University of Liverpool is committed to enhancing workforce diversity. We actively seek to attract, develop, and retain colleagues with diverse backgrounds and perspectives. We welcome applications from all genders/gender identities, Black, Asian, or Minority Ethnic backgrounds, individuals living with a disability, and members of the LGBTQIA+ community.

For full details and to apply online, please visit: recruit.liverpool.ac.uk

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