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computing and machine learning. The research will emphasize both theoretical advancements and practical implementations optimized for modern HPC systems. The postdoc will primarily contribute to one or more
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optimized to maximize/minimize one or more of the abovementioned metrics under certain constraints. However, very little work has been performed on system/network optimization while considering
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simulations and multiscale spatial-omics data. • Integrate uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments
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set by creating (unintended) by-products and waste. Individual optimization then leads to what we call a competitive circular equilibrium. On this new methodological basis, we will introduce concepts
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. The group will be contributing to the Physics Modeling (MC software, MC validation and Pileup modeling), the MET High-Level Trigger validation, optimization and performance studies, and to the heterogeneous
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valuable insights to help optimize device performance. Additionally, you will support collaborators by assisting with in-situ TEM measurements, facilitating cutting-edge research in sustainability and energy
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nanomaterial-nucleic acid hybrids for optimal device bio- and gas sensing, as well as for optoelectronic applications. About You The candidate will have experience in materials science research and ideally DNA
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without boundaries. Position Description Research Assistant will conduct research focusing on the following tasks: Creating and optimizing computational platform for the design of nanophotonic structures
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-support tools for decarbonizing mobile mining equipment. You will join an international multidisciplinary team. You will apply simulation, multi-objective optimization, and data-driven analytics to evaluate
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- Solid knowledge of existing literature in optimization and/or symbolic computation - Strong skills in programming with scientific and/or symbolic computing tools Website for additional job details https