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Field
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perturbations. This includes preprocessing large-scale single-cell and multi-omic datasets, defining model architectures, optimizing training pipelines on GPU clusters, and benchmarking against
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open source framework for adaptive mesh refinement, written in CUDA and supporting one-sided communication for scalability. The target architecture is a supercomputer with AMD MI300A GPU+CPU. 2
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perturbations. This includes preprocessing large-scale single-cell and multi-omic datasets, defining model architectures, optimizing training pipelines on GPU clusters, and benchmarking against existing
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foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM
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areas include: Parallel and distributed graph and or ML algorithms — shortest paths, connectivity, clustering, or graph traversal on GPU clusters and distributed-memory systems. Sparse direct and
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leadership-class computing systems and institutional clusters. Write robust Linux bash scripts and job submission scripts for SLURM and PBS environments, including multi-node GPU/CPU workflows, monitoring
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supercalculateurs et d'algorithmes adaptés. Les dernières générations de calculateurs font un usage intensif des accélérateurs de type GPU qui mettent à disposition une très grande quantité de calcul
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algorithms and running these models on high performance GPU computing clusters. The training will also provide knowledge about the recent advances in large language models. Mentoring: The
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/chemistry Usage of high-performance computing clusters, preferably GPU -based computing Proficiency in statistical analysis and hypothesis testing Strong publication record Our University Community
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, preferably GPU -based computing Proficiency in statistical analysis and hypothesis testing Strong publication record Our University Community Physical Demands Driving Requirements This position will never