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Field
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), including the use of HPC clusters Programming skills in data processing and hardware control (e.g. Python, Labview, Matlab, C++) Project management experience: setting goals, prioritising tasks, and meeting
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(HPC), or large-scale data analysis. Experience in applying AI/ML techniques to hydrological and Earth sciences. Proficiency in scientific programming languages such as Python, Julia, R, Fortran, or C/C
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pragmatic, iterative mindset - prioritising what works empirically and is testable over purely theoretical correctness. Valued: Experience with high performance computing (HPC). Prior exposure to mass
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strong asset; scientific programming in Python and/or C++ and use of HPC resources. Professional Experience: Demonstrated research on spin-orbit torques, evidenced by publications in peer-reviewed journals
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. Demonstrated programming ability and knowledge of Python and/or C++. Experience with deep learning frameworks like PyTorch and application on high-performance computing (HPC) environments using distributed data
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related field Excellent reacting flow multi-physics computation experience in high-performance computing (HPC) environments Experience in computational fluid dynamics (CFD) codes and modeling Ability
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professionals to accelerate scientific discovery and engineering advances across a broad range of disciplines. As an important part of the broader High-Performance Computing (HPC) infrastructure, the division
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related field. Compiler and Systems Expertise: Strong experience with LLVM (e.g., writing passes, IR transformations) and HPC programming in C++, MPI, OpenMP, CUDA, or related models. Solid understanding
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high-performance computing (HPC) environments Experience in computational fluid dynamics (CFD) codes and modeling Ability to present and publish results in peer-reviewed journal articles Preferred
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Python, ML, and AI for chemical applications Preferred: Familiarity with HPC systems Proven track record of research in ML/AI for chemistry Strong coding foundation (Python) Knowledge of C++ and CUDA