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recordings from human epilepsy patients and non-human primates are conducted using identical behavioral paradigms and combined with computational approaches. We are seeking an extremely motivated postdoctoral
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. Previous experience in computational modeling of atmospheric aerosols and parallel computing/software development is strongly desired. The term of appointment is based on rank. Positions at the postdoctoral
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Research Scientific Computing Center (NERSC ) at Berkeley Lab seeks a highly motivated Postdoctoral Researcher -- Scientific Machine Learning (NESAP) to join the Workflow Readiness team as part of NERSC's
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on finding and cultivating talented people who are passionate about what they do. Join us and be a part of the diverse Caltech community. Job Summary The Research Assistant in Scientific Computing will work
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project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain expertise in areas like computational
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their own research program in collaboration with, and in parallel to, Prof. Zanazzi. Penn State hosts a vibrant community of scientists working on many aspects of exoplanetary astrophysics, including
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Theoretical Physics or a related discipline completed within the last 5 years. Experience with High Performance Computing and programming for massively parallel computers. Experience with quantum many-body
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to analyzing data Knowledge of high-performance computing, such as parallelization, the use of C++, or interfacing with specialized linear algebra packages Other Information: Work arrangement: On-site Candidates
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differential equations. The individual must be able to immediately program in the software SFEMaNS using object-oriented Fortran 2033 and MPI. The individual must be familiar with the linear algebra parallel
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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