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to thrive and become sought-after experts in SciML, with numerous opportunities in academic, government, and industry positions. The position is offered for one year, continuation is contingent upon funding
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
PhD in a relevant field such as structural mechanics, heat transfer, numerical optimization, topology optimization, or lattice design. The postdoctoral scholar will be responsible for vigorously
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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
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CO2 capture from the atmosphere. Your objectives will include to: Develop new optimization and/or machine-learning based reconstruction and segmentation algorithms to improve image quality in time
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. The HEXAPIC project aims to develop a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimizing PIC algorithms
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transient electromagnetic (TEM) data. A key task will be to conduct numerical sensitivity analyses for potential acquisition protocols employing both FEM and TEM data, with an eye towards optimizing field
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digital twins to develop innovative solutions for monitoring, analyzing, and optimizing urban systems in real time. The candidate will contribute to modeling interactions between physical and digital
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of novel strategies for neuronal analysis in health and disease and optimizing novel methods. The project is funded by My Name’5 Doddie Foundation, as part of their Catalyst Awards. Using iPSC-neurons, our
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to the above requirements • Strong background in optimization and partial differential equations • Strong background in numerical mathematics and computing • Machine learning skills are welcome • English skills
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in adaptive immune systems (e.g., co-evolution of bacteria and phages, as well as T and B cells with pathogens). • Physics-informed machine learning of biophysical systems (e.g., developing optimal