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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and
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working in High-Performance Computing (HPC) environments. Soft Skills: Proven track record of scientific publishing and the ability to mentor junior researchers (EngD/PhD students). Mindset: An analytical
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managing the associated procurement activities for their cloud- or HPC-based implementation; supporting the operational Copernicus Sentinels missions and DestinE data management activities; supporting
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. The project has access to the national computing infrustracture, TU/e HPC cluster SPIKE-1 , ASML HPC cluster, ASML datasets, and potentially custom data through collaboration with e.g. IMEC. Where to apply
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, project-based innovation, and specialist support come together. Among other things, the teams offer access to HPC and HTC environments, storage, processing and analysis capacity for large-scale datasets
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the digital research infrastructure in the social sciences. The data infrastructure is internationally unique, including state-of-the-art supercomputer and HPC facilities, data collection, and support for
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publications at top-tier venues such as CVPR, ICCV, ECCV, NeurIPS or ICRA. You will have access to extensive compute resources at TU Delft, ranging from local GPU servers to large-scale HPC infrastructure
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; additional experience in one or more of the following areas is highly desirable: stochastic simulations quantitative genetics breeding programs working with Linux and HPC systems For this position your command
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is highly desirable: big data analytics quantitative genetics variance component estimation working with Linux and HPC systems For this position your command of the English language is expected to be
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workflows. Familiarity with Linux/HPC environments (for the modeling position). Experience with data visualization or handling large datasets. Demonstrated interest in climate physics and/or cross