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Field
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Python and C/C++. Expertise in ensemble learning (e.g., Random Forests, Gradient Boosting, bagging/stacking frameworks). Hands-on experience with parallel or GPU-based computing (CUDA, OpenCL
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programming language to solve computational problems Awareness of computational infrastructure and its upkeep Ability to work on multiple projects in parallel and set priorities Willingness to provide training
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focused on the optimal utilisation of advanced RF sensors that are capable of performing multiple roles, either sequentially or in parallel. The UCL Radar Group hosts one of the few demonstrator systems
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workloads. Conduct research on language front‑end abstractions, mixed‑precision modeling, heterogeneous parallelism, and MLIR-level transformations. HPC System Co‑Design: Work with domain scientists and
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department in its field, running a globally competitive programme of research and teaching. The Botnar Research Centre enables and encourages research and education into the causes of musculoskeletal disease
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adapting large-scale codes developed for parallel computing. Record of excellence in professional achievement, as evidenced by a strong publication record. Demonstrated ability to carry out independent
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-SofT project, the recruited person will: - design novel algorithm for large-scale block-sparse tensor computations that are suited for massively parallel accelerated supercomputers. - investigate the use
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parallel screening platform to discover orthogonal protein binders. In addition to carrying out research, the successful candidate will be expected to apply for fellowship funding, contribute to the writing
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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computing software libraries (e.g., Trilinos, MFEM, PETSc, MOOSE). Experience with shared and distributed memory parallel programming models such as OpenMP and MPI. Experience with one more GPU or performance