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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
experience programming in the Unix/Linux environment using Python, Java, C/C++, or Julia; must have experience with algorithms, numerical techniques, and computational methods, specifically for uncertainty
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theoretical models and methods as well as in implementing numerical optimization techniques Interest in working closely with experimentalists Detailed knowledge of quantum physics and experience with quantum
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mining waste deposits (MATs). This project integrates mineralogical and mechanical characterization, pilot-scale testing, and advanced process simulation, with the objective of optimizing grinding
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collaboration with business and industry, and numerous researchers from the department have established companies to develop new medicinal treatments founded in professional scientific basic research. You can
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uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in
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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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Associate with mathematical modelling and numerical/data analysis background to join our food system resilience project, led by University of Reading, joining a large interdisciplinary team with an excellent
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contribute to the activities including TES unit development, laboratory testing and techno-economic analysis to identify optimal integration opportunities. Cooperation with industrial and academic national and
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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advancements and practical implementations optimized for modern HPC systems. The postdoc will primarily contribute to one or more of the following research areas: Development of efficient numerical linear