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: Comparative transcriptomics, orthology inference, positive selection detection, protein domain analysis, phylogenetic comparative methods Computational skills: UNIX/Linux, HPC computing, R, Python You will gain
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-performance computing (HPC) facilities and finite element codes. Project findings could have a notable impact on the deployment of green hydrogen infrastructure, as needed to achieve net-zero carbon ambitions
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and debugging, as well as software engineering best practices. You will have access to cutting-edge computational resources, including the latest Imperial College HPC clusters, and UK National
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partners. Training & environment You’ll gain deep skills in hypersonic flows, AI for PDEs, data assimilation, and reproducible HPC workflows (Python/C++/PyTorch/JAX). You’ll be supported with paper writing
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associated numerical methods and AI, will be used with High Performance Computing (HPC) to improve understanding of key flow physics and inform future HPT design. Skills and Experience Required: Applicants
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HPC clusters. Experience of modern computational statistical methodologies, such as Markov chain Monte Carlo, rejection sampling, sequential Monte Carlo, would be highly desirable. Attributes and
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. Bioinformatics: Comparative genome analysis, detection of selection, and functional genomics, phylogenetics. Computational skills: UNIX/Linux, HPC computing, and programming in R and Python. You will gain hands
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Evidence of conducting independent, high-quality research High level problem solving and analytical skills Strong programming skills (R or C) Desirable Experience of using HPC clusters Experience of modern