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Field
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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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: Strong understanding of statistics, probability, optimization, and linear algebra. - Machine Learning: Deep learning, probabilistic modeling, generative models, etc. - Programming & Software Development
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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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research experience. Strong mathematical fundamentals and algorithms skills or experience. Excellent programming, debugging, performance analysis, and test design skills. Ability to work independently
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solvers, e.g. UVLM, DLM, hybrid 2.5D/VLM coupling, free wake methods and vortex particle solvers; or Experience with high-fidelity solvers, e.g. SU2, OpenFoam, StarCCM+, Fluent; Proficiency in programming
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McGill University | Winnipeg Sargent Park Daniel McIntyre Inkster SE, Manitoba | Canada | about 14 hours 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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software for analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical.]; data management and analysis solutions that assist in storage, investigation and dissemination
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Application Deadline 14 Mar 2025 - 00:00 (Europe/Madrid) Type of Contract Permanent Job Status Part-time Hours Per Week 22,5 Is the job funded through the EU Research Framework Programme? Horizon Europe
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Research Centre (EPRC). EPRC research covers a range of topics including energy demand, electricity markets, and energy efficiency. In 2017, the ESRI initiated a new, parallel research programme on Climate