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position within a Research Infrastructure? No Offer Description The position targets the design and evaluation of novel architectures, programming paradigms, and parallel algorithms for AI accelerators
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 15 days ago
development of methods for acceleration of parallel applications in the High Performance Computing (HPC) environment, · work in the international team focused on development and optimization of parallel codes
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 15 days ago
for acceleration of parallel applications in the High Performance Computing (HPC) environment, · work in the international team focused on development and optimization of parallel codes under national and
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algorithm development and application. Familiarity with common scientific programming languages such as C++. Experience in parallel programming with one or more common parallel programming models, like MPI
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- and neuron-targeting anti-obesity drugs in these ex vivo systems. In parallel, you will collaborate with a team of postdocs, PhD students, and technicians working with genetic animal models
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and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data
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to prevent the onset of cancer. In parallel, our research is dedicated to advancing our understanding of bladder tumor evolution biology through the analysis of next-generation sequencing (NGS) data and the
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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; or Experience with high-fidelity solvers, e.g. SU2, OpenFoam, StarCCM+, Fluent; Proficiency in programming, e.g. Python, Matlab, C; Experience utilizing high-performance computing (HPC) to parallelize workflows
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
and Experience: Distributed parallel training and parameter-efficient tuning. Familiarity with multi-modal foundation models, HITL techniques, and prompt engineering. Experience with LLM fine-tuning