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This is a theoretical/computational postdoctoral position for the prediction and development of strongly correlated materials for use in quantum technologies. Project activities include
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focusing on the use QM/MM simulations to study targeted covalent inhibition and approaches to accelerate quantum chemistry calculations on quantum computers. Candidates should have a PhD in computational
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institutions, and leading industry partners. The successful candidate will contribute to the delivery of high-impact research projects involving AI algorithm evaluation and image data analysis. You will play a
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-holomorphic Hilbert Modular Forms”. The central aim of the project is to develop explicit algorithms for computing with non-holomorphic Hilbert Modular Forms and using these algorithms together with theoretical
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of structural health models. - The mechanical testing of materials using standard and non-standard test piece geometries for the development of material constitutive models, and the mechanical testing
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tackling foraging tasks, to work out which algorithm best explains how foragers decide to leave. You will have some experience in computational modelling of behaviour, such as decision-making models
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10 minutes and machine learning algorithms to deliver quantitative diagnosis without destroying the samples. The AF-Raman prototype will be integrated and tested in the operating theatre
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, alignment, and characterisation of laser-based instrumentation. • Strong experience with computer programming, both for signal processing and experimental hardware control (e.g., C, C++, Python, MATLAB
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friendly and highly experienced team undertaking a qualitative research and educational development project funded by the Burdett Trust for Nursing, Women’s Health Grant Programme. The project seeks
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technologies are critical enabling components for a wide range of QT based applications including quantum sensing, timing and computing To achieve this your role will involve developing inverse methods and