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Strong foundation in CFD, Programming proficiency such as Python, AI/ML techniques, Experience with parallel computing on CPU/GPU cluster, use of CUDA, MPI is a plus. Experience Experience with open-source
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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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The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational
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for the quality of its medical expertise, facilities and teaching. The Neuroscience and the Mental Health Program is one of the leading research programs in the medical school. The key objective is to conduct both
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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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. Among the methods used are high-throughput CRISPR screening, protein deep mutational scanning, massively parallel reporter assays, and genetic manipulation of cell lines and mice. The successful candidate
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: Proficiency in Python, PyTorch, JAX, or other ML frameworks - Computing: Experience with large-scale datasets, parallel computing, and GPUs/TPUs. - Algorithm Development: Ability to develop and optimize Machine
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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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field or method, including, but not limited to, numerical methods, machine learning, or parallel and distributed computing. Expertise in a parallelization method (e.g., CUDA or ROCm, MPI, OpenMP