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Field
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into that framework, and to take advantage of these tools to produce optimal designs. Applicants should have skills in modelling, familiarity with partial differential equations, and be familiar with python. They will
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that integrate simulation, machine learning, and data analysis. Numerical optimization methods (e.g. machine learning including deep neural networks, reinforcement learning, data mining, genetic algorithms
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programme of research and knowledge exchange to develop and optimize new testing regimes for flood resilience products, enable PFR needs to be aligned with exposure to flood risk and formulate an evidence
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such as PyTorch and TensorFlow. Experience with high-performance computing and/or scientific workflow. Strong background in inverse problems, numerical optimization and image processing. Job Family
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, collaboration, high-quality work, and real-world problem solving. This position will conduct numerical simulation studies, work on research projects with external partners, mentor and guide graduate student
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programme of research and knowledge exchange to develop and optimize new testing regimes for flood resilience products, enable PFR needs to be aligned with exposure to flood risk and formulate an evidence
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water quality. You will be embedded within the Ethio-Nature project, funded by the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use
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power (CSP) systems optimization, computational heat transfer and radiative transport using sophisticated numerical modeling and machine learning approaches for forward and inverse problems in radiation
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. Key Responsibilities: Quantitative in situ and in vitro imaging studies related to cytokine signaling and cell-cell interactions. Design and optimization of multiplexed fluorescence imaging, in situ
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are As one of the fastest-growing academic health centers in the nation, Texas A&M Health encompasses five colleges and numerous centers and institutes working together to improve health through