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Field
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learning and simulation-based inference for searches for dark matter (or other “invisible” new physics signals) at the Large Hadron Collider, with the support of competent and friendly colleagues in
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to represent PVD-functionalized electrodes without unnecessarily increasing computational cost. The work will combine fundamental modelling, numerical simulation and experimental validation, with a strong
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candidate who has a strong quantitative background, with a PhD in Computational Mathematics, Applied Mathematics, Computational Mechanics, Geosciences, Acoustics or Simulation engineering. The candidate
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challenging, especially in dunes with complex topography. Your job In this 2-year postdoc position, you will use numerical modelling and machine learning techniques to increase the accuracy of coastal dune
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/or oceanography; or experience working with numerical simulations, large data, scientific programming, and/or machine learning. Applicants are asked to send a CV, a brief statement of research
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problems in bioinformatics. This project is part of the Faculty of Science's new Quantum Simulation for Molecular and Material Design (Q-MMD) programme. You will join a growing interdisciplinary team at
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of hydrogen storage systems for energy applications. The postdoctoral researcher will contribute to: Development and implementation of advanced multiphysics numerical models Simulation of sorption phenomena and
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to: Design, model and optimise microwave circuits, antennas and antenna systems for CubeSat and related space applications. Undertake full-wave electromagnetic simulation and numerical modelling of microwave
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phenotypes, and high-performance computing infrastructure. Applicants must hold a PhD in human genetics, genetic epidemiology, biostatistics, computational biology, or a related field, with a strong
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supercomputing facilities. Knowledge of numerical methods for large-scale simulations of fluid flow, heat transfer, and chemically reacting systems. Documented ability to work effectively in interdisciplinary