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to) SIESTA (www.siesta-project.org) and its TranSIESTA functionality. SIESTA is a multi-purpose first-principles method and program, based on Density Functional Theory, which can be used to describe the atomic
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tools in combustion. Our computational codes are also used by various international research institutions. Both experimental and numerical projects are conducted in parallel providing a platform for
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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optimization, with experience in adaptive routing and SDN technologies. Proficiency in programming languages such as Python, C/C++, and experience with parallel computing frameworks. Effective written and oral
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. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages such as python. Experience with HPC environments and linear algebra
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Budget Preparation and Management Develops, prepares, and manages WTL 2.0 budget as training under the guidance of the PIs. Monitors expenditures in parallel with Dr. O’Hara Orders supplies to maintain key
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The Robot Learning Lab in the Department of Computing at Imperial College London is seeking a talented Research Assistant (pre-doc) for an ambitious new project on dexterous robot learning. In
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written communication skills. A collaborative mentality towards research and mentoring. Desired skills and techniques include experience in parallel computation and advanced numerical programming, using
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Global Critical Zone Science Chair to develop and conduct a research program to better understand forest nutrition and nutritional stress in Eucalyptus forest stands in Brazil. Research context: Forests
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turbulent combustion applications, as well as parallel scientific computing. Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and