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Field
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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Interactions external link groups, with close interaction with bioinformatics, microbial ecology and experimental crop research at the UU and NOAH partners. You will have access to Utrecht University GPU/HPC
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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
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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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-to-end GPU timing; document limitations and extrapolation behavior. Implement, test, document, and maintain open-source Python/JAX research software; collaborate with researchers to connect trained models
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LLMs en infraestructuras GPU. - Análisis y evaluación de explicabilidad de las respuestas generadas por la IA generativa Area: Data Science and Natural Language Processing Tasks: - Development
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for career development Access to high-performance computational resources (with GPUs) A collaborative environment across research fields, including plant biology, quantitative genetics, and population genetics
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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/ coarse- grained approaches) Experience with enhanced sampling techniques; computational biophysics/chemistry Usage of high-performance computing clusters, preferably GPU-based computing Proficiency in
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heat transfer processes Use of advanced numerical methods (CFD, LBM, hybrid models) Utilization of high-performance computing (HPC, GPU) Analysis and validation of numerical results Optimization