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experimentation and thermodynamics calculations. Establish a high-throughput bulk materials processing route to enable efficient characterisation and parallel testing of multiple compositions. Develop a high
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in a dynamic and collaborative team. In collaboration with the Edinburgh Parallel Computing Centre (EPCC) and our industry partners, the focus of the role is the development of a new solver for
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frameworks (e.g. PyTorch, TensorFlow) and relevant libraries. Practical experience in scalable data processing, including the use of parallel computing, cloud platforms, and distributed systems for efficient
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, e.g. Python, Matlab, C; Experience utilizing high-performance computing (HPC) to parallelize workflows; Excellent work planning and issue resolution skills; Strong technical, written, and verbal
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methane exchange in upland trees drawing on information derived from parallel field studies spanning a rainfall gradient in Ghana (and elsewhere) and modify empirical models of tree methane exchange