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in AI to study natural and artificial minds in parallel, creating the opportunity to make discoveries about ourselves and to find new ways to understand and improve AI systems. Appointments will be
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sampling of the parameter space of eclipsing binary observables, most notably photometric data from NASA’s Kepler and TESS missions. In parallel, the applicant will be given an opportunity to teach
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Postdoctoral Fellow - Materials Chemistry, Texas Materials Institute, Cockrell School of Engineering
: UT MAIN CAMPUS ---- Job Details: General Notes As a top-10 engineering school with the No. 1 program in Texas, the Cockrell School of Engineering at The University of Texas at Austin has been a global
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Readiness team as part of NERSC’s Science Acceleration Program (NESAP ). You will join a multidisciplinary team building AI-driven scientific workflows for the upcoming Doudna supercomputer. Doudna will
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engineering or a related field (including computer science/engineering) Prior experience with computer programming Prior experience with CFD modeling Develop and implement models for multiphysics modelling in
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Science Acceleration Program (NESAP ). You will join a multidisciplinary team building AI-driven scientific workflows for the upcoming Doudna supercomputer. Doudna will deliver over 10x the performance
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appointment, with the possibility to be renewed for additional research periods. Appointments may be extended depending on funding availability, project assignment, program rules, and availability
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surgery and surgical critical care to successfully integrate APPs into a career in surgery or critical care, while improving the life of every patient. The program is designed for both Physician Assistants
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will join Andrej Prsa’s research team and work on the PHOEBE code , advancing our understanding of the processes in contact binary stars. In parallel, the applicant will be given an opportunity to teach
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has also been developing physics-based machine learning algorithms for three dimensional seismic modeling, imaging and inversion using high performance computation including parallelization on GPUs