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discipline. Demonstrated hands-on experience and understanding of developing and applying HPC algorithms to sparse numerical, scientific and ML models. Demonstrated research experience with AI and ML
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. outside of HPC environments) Downloading a copy of our Job Description Full details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided
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months or 0.8 FTE for 45 months; access to computational resources (HPC), GIS/data infrastructure, and datasets via collaborative networks; a supportive, interdisciplinary research environment within
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with high-performance computing (HPC) infrastructures is advantageous Excellent analytical and problem-solving capabilities Proven track record of publishing in reputable scientific journals and at
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-performance computing (HPC) environment Perform data analysis and visualization Perform machine learning and inverse design techniques Train and supervise masters and doctoral students Coordinate research with
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. This role will involve the development of novel lattice QCD algorithms and high-performance computing (HPC) codes, and/or exploring applications of artificial intelligence (AI) to lattice simulations
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computing (HPC) development of SeA (in collaboration with the DiStasio research group at Cornell University) and the broader QE package. We also expect this position to offer many other collaborative
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, or computational biology Proficiency in Python and experience working in Linux-based HPC environments or cloud computing platforms Proven experience with deep learning frameworks such as PyTorch or TensorFlow, and
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-house CFD software packages. (3) Designing and developing CFD sub-models for application to a broad range of CFD problems. (4) Using high-performance computing (HPC) to accelerate complex, large-scale
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spatial distribution of critical topsoil properties in global drylands. Process large-scale geospatial and remote sensing datasets using High Performance Computing (HPC) systems. Conduct data analysis, and