Professor/SL and Theme Lead AI for Materials (CMI)

Job Description

The Centre for Machine Intelligence (CMI) at The University of Sheffield wishes to recruit a Professor/Senior Lecturer (Full professor/Grade 9, equivalent to Associate Professor) and Theme Lead in AI for Materials.

The CMI is a strategic initiative supported by a £3.64m investment, dedicated to the transformation and acceleration of research, innovation, and teaching on and with AI. This position is one of four academic theme lead appointments. You will work across the CMI, the School of Chemical, Materials, and Biological Engineering (CMBE) and School of Computer Science (COM) for the first three years of the post, after which you will be placed in a School in the Faculty of Engineering appropriate to your disciplinary background and expertise. While focusing on AI for Materials specifically, you will also lead a broader AI for Engineering and Manufacturing theme within the CMI.

AI for materials is a key research theme at the University of Sheffield and there are tremendous opportunities for growing research with the potential for significant impact, for example, via our large-scale translational research centres, such as the world-class Royce Discovery and Translational Centres, and our Advanced Manufacturing Research Centre (AMRC).

New materials are essential for tackling major human challenges, such as electrical propulsion, power generation, high performance computing, and energy storage. Discipline-specific AI techniques and expertise at the materials/AI interface are required to maximise the tremendous potential of AI for rapidly accelerating materials discovery and development. Particular areas of focus for the theme lead include:
• Moving from discovering possible materials to AI making intelligent predictions of application-specific material properties and performance.
• Physics-aware AI, capturing complex inter-relations between manufacturing, microstructure and properties in real materials.
• Natural language processing for materials discovery and properties prediction
• Interpretable and explainable AI and uncertainty quantification in the context of materials and manufacturing in general.
You will have a background in computational research with materials including a track record in machine learning /AI methodology and its application. You will have direct experience of interdisciplinary collaboration using data from across relevant sectors. You will have well-developed leadership skills, and be able to collaborate effectively with other theme leads and the Centre Director to ensure that the CMI is at the forefront of AI research, innovation, and impact, nationally and internationally.

Your teaching load during the first three years of this position will be reduced to a maximum of 20% allocated workload, to ensure that you have capacity for leadership activities. The position is supported by a PhD studentship (UK fees and stipend for 3.5 years) and a generous start-up package for travel and equipment.

This role is supported by AI Futures Grants, a UK Government scheme designed to help the next generation of AI leaders meet the costs of relocating to the UK. AI Futures Grants provide financial support to reimburse relocation costs such as visa fees, the immigration health surcharge and travel/subsistence expenses. Successful candidates for this role may be able to get up to £10,000 to meet relocation costs, subject to terms and conditions, in addition to University of Sheffield relocation support.

We’re one of the best not-for-profit organisations to work for in the UK. The University’s Total Reward Package includes a competitive salary, a generous Pension Scheme and annual leave entitlement, as well as access to a range of learning and development courses to support your personal and professional development.

We build teams of people from different heritages and lifestyles from across the world, whose talent and contributions complement each other to greatest effect. We believe diversity in all its forms delivers greater impact through research, teaching and student experience.

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