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Post-doctorate position (M/F) : Exascale Port of a 3D Sparse PIC Simulation Code for Plasma Modeling
degree / PhD computer science or physics with high-performance computing - Experience in Fortran, C or C++ programming - Experience in high-performance computing and parallel programming, in particular GPU
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invasive sensing tools to monitor metabolites, oxygen, carbon dioxide, pH, and other parameters. Ideally, the methods can function in parallel and on a large scale. The research is vital to understand key
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PhD degree in Computer Science, Physics or a related field Experience with parallel programming models Strong programming skills in C/C++ and/or Python Knowledge of distributed memory programming with
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will consist in the porting and optimization of parallel and heterogeneous programming models for the upcoming multiprocessor systems developed in the DARE project . The focus of DARE are long vector
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of laminar/neuropixel probes and electrical microstimulation to study attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH
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generative design tools such as HEEDS, and/or the Dakota or RAVEN uncertainty quantification tools. Experience with FORTRAN, C, and/or C++ applied programming. Knowledge of Python, Java, or other scripting
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Identify new applications for Machine Learning in science, engineering, and technology Develop, implement and refine ML techniques Implement parallel ML training on the High Performance Computers Engage in
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• Familiarity with operating HPC clusters (e.g., bash, Python) Preferred Qualifications • HPC programming skills (e.g., modern Fortran or C/C++) • Parallel programming skills (e.g., OpenMP, MPI, OPENACC, CUDA
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McGill University | Winnipeg Sargent Park Daniel McIntyre Inkster SE, Manitoba | Canada | 6 days ago
imaging system based on the random access parallel imaging (RAP) system platform (https://doi.org/10.7554/eLife.56426 ). The RAP platform quickly collects high-contrast images in multi-well plates using
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Knowledge in modeling and algorithms for large-scale ordinary differential equations (ODEs) and differential-algebraic equations (DAEs) Proficiency in a scientific programming language (e.g., C, C++, Fortran