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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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access to state-of-the-art numerical models and high-performance computing systems at Princeton and in NOAA, working alongside GFDL model developers and software engineers to advance quality assurance and
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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
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the problem is explicitly considered. In particular, it will investigate how to tightly integrate state-of-the-art sampling-based methods with state-of-the-art methods from numerical optimal control in a
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novel theoretical frameworks for atom-based quantum computing, including but not limited to: o Quantum error correction, fault tolerance, and resource optimization. o Entanglement dynamics, quantum