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-fidelity industrial simulation. Engineering applications include Formula 1, turbomachinery and nuclear fusion. These problems exhibit complex geometries and challenging time-varying physics which require
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-performance computing, simulations, and computational methods in physical sciences. Informal enquiries should be directed to the project supervisors, Dr Cillian Cockrell (Bangor University, NFI – c.cockrell
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methods to enable efficient structural simulation of novel aircraft configurations – essential as aviation transitions to alternative fuels. These methods will also expand the role of simulation in
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engineering, or atmospheric science* Expertise in and passion for computational modelling and software development/engineering Expertise in cloud physics or contrails preferred but not required Creative problem
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allowed computational fluid dynamics (CFD) to flourish, becoming an indispensable for many industries. Simulating the full Navier-Stokes equations is computationally prohibitive for most applications, so
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will use a high-fidelity large eddy simulation (LES) code and scientific machine learning tools, such as real-time optimisers, in order to simulate wind farms exposed to various atmospheric inflows. Some
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biological materials. The development of novel computer-vision-based techniques for contactless detection, quantification, and prevention of sport injury. The development of robotic humanoid simulator and
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-performance computing and simulation-based inference (e.g. neural network emulators or nested sampling) Training will be provided in all aspects of the project, including computational statistics, stellar
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pathways, whilst their omission can introduce significant systematic biases in parameter estimation, detection pipelines, and tests of fundamental physics. In this PhD project, you will develop a
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About Us The applicant will join the new Wellcome-funded Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. The post will