51 molecular-modeling-or-molecular-dynamic-simulation Fellowship research jobs at University of Birmingham
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, and AI-interviews". Our research programme aims to address persistent opioid usage by identifying molecular, neurobiological and cognitive signatures characterizing patients at risk of prolonged opioid
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in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
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interdisciplinary Interest Groups serving as testbeds to explore new and emerging areas of interest, we are a dynamic place to work. We have ambitious plans to expand our research activity and are in an exciting
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the growth of the company. The need is to develop significantly larger system designs and simulations and to embed the added knowledge into the business as a legacy capability to reduce the reliance
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. Background Applications are invited to join the Sun, Stars and Exoplanets Research Group in the School of Physics and Astronomy at the University of Birmingham, as part of a growing and dynamic team working
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apply advanced computational tools and reproducible workflows to interrogate large-scale, liquid chromatography–mass spectrometry (LC–MS)-based comparative metabolomics datasets spanning a range of model
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guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from research Contribute to Departmental
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funding scheme, supporting new collaborative opportunities. Promotes equality and values diversity acting as a role model and fostering an inclusive working culture. Research and Dissemination: Propose
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interpretation. This role will involve working closely with civil engineers, computational modellers, Physicists, and geophysicists to translate raw sensor outputs into actionable insights. Role Summary Lead data
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can be leveraged to accelerate learning from both classical and quantum data. The project will develop rigorous theoretical frameworks to understand key properties of quantum machine learning models