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machine learning libraries. Familiarity with collaborative coding environments (e.g., Git) and working on high-performance computing (HPC) clusters is an advantage. Good scientific writing and communication
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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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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