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meetings and Super Computing Conferences, including assisting with booth management. Participates in research when the opportunities present itself. Assists in development of IRCC procedures and processes
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or exceeds these requirements while expeditiously furthering the research objectives of the Department of Dermatology. This position must be able to work independently on multiple research projects in parallel
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algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML
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advanced genome-editing tools. In parallel, we develop in-house sequencing technologies to dissect epigenetic and epitranscriptomic modifications at the molecular level, along with high-throughput platforms
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& Responsibilities: Implements: Algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical]; Data management and analysis solutions that aid in
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systems based on massively parallel hardware architectures Combination of programmable logic, tensor processors and general-purpose CPUs for real-time adaption and scheduling services (e.g., AMD Versal
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on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed optimization of machine learning workflows. Collaborating
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | about 1 month ago
-09731 Requirements Skills/Qualifications Candidate profile - Master degree Computer Sciences. - Experience in scientific programming (e.g., Python, C++, Fortran, Julia) and parallelization
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algorithms will be assessed for neurodegeneration mapping in Alzheimer’s disease brain organoids. In parallel, the technology and algorithms will be applied to fish health research, supporting studies on ulcer
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources