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Field
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turbulent flow simulations. 3. Background in numerical methods and scientific computing. 4. Proficiency in Python programming and high-performance computing on modern GPU-based platforms
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analysis, profiling, and optimization of parallel applications using tools such as NVIDIA Nsight, rocProfiler, or similar. Strong understanding of parallel programming with MPI, OpenMP, and GPU-based
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compensation and working conditions. Basic Qualifications Ph.D. or M.D./Ph.D. in areas such as machine learning, computer science or closely related field. Excellent programming skills and practical experience
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Systems and Software Program (WASP ). You can find more information about us on the Department of Information Technology website. The position is hosted by the Division of Scientific Computing (TDB), one
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computational modelling of additive manufacturing and develop high-performance GPU-based CFD solvers. Qualifications • With PhD degree • Strong research experience in developing GPU-based
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/scientific computing and numerical methods for PDEs High‑performance computing (parallel distributed programming, GPU programming) Astrophysical fluid dynamics and/or magnetohydrodynamics Radiative transfer in
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architectures; • strong programming skills in Python and experience with a deep-learning framework such as PyTorch, including training and evaluating models on GPU/HPC infrastructure; • experience working with
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GPU infrastructure. This Postdoc position is part of the eSSENCE graduate school in data-intensive science. The school addresses the challenge of data-intensive science both from the foundational
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interfaces. Plan controlled ablation studies and reproducible evaluations using scratch training across multiple seeds, exact train and validation metrics, parity and error analyses, gradient checks, and end
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computing environments and GPU computing. Proven experience in weather and climate models development and applications. Experience in machine learning, deep learning, or AI applications for atmospheric