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Title: Modelling dynamics and reactivity with Machine Learning Interatomic Potentials Supervisor: Professor Fernanda Duarte Start date: 1 st October 2027 Applications are invited for a fully funded
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. Previous experience in magnetism, spin dynamics, nanofabrication, electrical measurements, 2D materials, programming and machine learning would be advantageous. Candidates should also be able to work
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January 2027 Title: Modelling dynamics and reactivity with Machine Learning Interatomic Potentials Supervisor: Professor Fernanda Duarte Start date: 1 st October 2027 Applications are invited for a fully
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responsibility for carrying out research in rough path theory, machine learning, generative AI and related fields as part of the DataSig II grant “High order mathematical and computational
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expansion, and machine learning. This work will have strong links to the experimental synthesis, diffraction and electrochemical work performed in the overall project. Previous experience in atomistic
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methodologies, including machine learning and artificial intelligence, to advance fundamental understanding, and engineering, of problems arising in the physical, biological, medical, and information sciences
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discipline (e.g. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely related field) with significant post-qualification research experience. You
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, electrical measurements, 2D materials, programming and machine learning would be advantageous. Candidates should also be able to work effectively both independently and as part of a collaborative research