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training and inference of GMMs for large, high-dimensional datasets Explore parallelization strategies to leverage modern GPU architectures Benchmark GPU-based implementations against CPU-based approaches
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hold weekly in-person or remote office hours (one hour per week). Additionally, instructors will attend weekly planning meetings to coordinate between parallel sections taught on different days. NEUR 102
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. utilities, libraries, plugins) Experience with parallel file systems (e.g. Lustre, GPFS) Experience in high performance computing (HPC) environment. Experience with configuration management software such as
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critical responsibilities. Key duties include microscopic FISH analysis, karyotyping analysis utilizing computer systems, FISH wet lab procedures, slide hybridization, PET slide preparation, and proficiency
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, and parallel computing, with a proven ability to work within highly secure and regulated environments. This role involves close collaboration with security teams, scientists, and IT leadership to ensure
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their compilation tools in different environments. (C++, Fortran, ...) Experience with parallel computing, cloud deployment, and network license managers (FlexLM, RLM, etc.). Strong background in
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user support. Familiarity with scientific software, Linux systems, and parallel computing frameworks. Special Requirements: Q or L Clearance: This position requires the ability to obtain and maintain
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Requisition Id 15601 Overview: The Analytics and AI methods at Scale (AAIMS) group in the National Center for Computational Science (NCCS) is hiring Senior Research Scientist to push the frontier
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computational, molecular and biophysical techniques. During the project you will design the methodology for, and perform the directed evolution of H-transfer enzymes using parallel selection methods, measure
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 22 days ago
, embeddings with transformers, training with flow matching) and high performance computing (e.g. handling large-scale parallel simulators, multi-node and GPU training on large supercomputers). When considering