42 postdoc-computational-fluid-dynamics Fellowship positions at University of Nottingham
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This is a theoretical/computational postdoctoral position for the prediction and development of point defects in two-dimensional materials for applications in quantum technologies. Project
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conduct the research activities into the computational fluid dynamics simulation and optimisation of vortex reactors. You will develop physical and numerical models for the three-dimensional simulation
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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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(hiPSCs), cardiomyocytes, metabolism About the project We are recruiting a postdoc (established or newly graduated from their PhD) who has an exceptional ‘can-do’ attitude, with drive and enthusiasm to push
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materials, notably wear, fretting, and thermo-mechanical fatigue. Experimental studies to support these modelling activities are also of great interest to the group. Visualisation of multiphase fluids with
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deployment, ensuring that all deliverables meet high standards of performance, scalability, and user experience. This is an exciting, dynamic position offering the chance to engage with cutting-edge
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to an exciting new effort to accelerate and miniaturise micro-elasticity imaging systems for in-vivo and clinical applications. You will be part of a dynamic, interdisciplinary team aiming
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for independent research into the prevention and treatment of skin disease. You will join a dynamic and friendly team of approximately 20 staff and work with a range of clinical and non-clinical academics
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programme. To acquire, analyse, interpret and evaluate research findings/data using approaches, techniques, models and methods selected or developed for the purpose. To establish a national reputation and
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entitled “White Matter Computation: Utilising axonal delays to sculpt network attractors”. The central aim of the project is to determine how dynamic patterns of neural activity evolve in a complex network