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, and development of algorithms for real-world clinical data. The fellow will have the opportunity to work with clinicians, engineers, data scientists, trainees, and research staff in a highly
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distribution. Both projects will develop the Fontan emulator to be patient-specific, MRI compatible, computer controlled, and quantitatively evaluated using advanced bench-top, CFD/FSI simulation, and MRI-based
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of reaction pathways, residence-time distributions, heat and mass transfer, and particle or catalyst evolution. Establish quantitative mass, carbon, and energy balances across the reactor system. Relate reactor
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scientific domains. Preferred Experience: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling
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· database extraction · data cleaning · analysis · algorithm development and implementation · data visualization [CI1] The postdoctoral
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the team with compiling and analyzing geospatial datasets, developing and testing deep learning algorithms for complex image classification, optimizing data collection strategies for unmanned aerial vehicle
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work is robustly extending genomic discoveries into under-represented populations, as well as improving portability of genetic risk prediction algorithms into the same populations in anticipation
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independently and to mentor junior researchers, students, or trainees. Ability to collaborate effectively with a distributed, multi-institutional team. Experience teaching complex material to trainees