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research resource for the department, ensuring reliable study execution and operational support for faculty research. In parallel, the postdoc will advance their own research program and collaborate with
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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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Engineering, Computer Engineering, or Computer Science 2. Demonstrated, successful experience (academic or professional) programming in algorithm level languages such as Matlab/Simulink, Python, C# or similar 3
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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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with implementation of: existing algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical.]; data management and analysis
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computers (e.g., parallelizing and distributing code). Experience in distributed data management and workflow systems. Preferred Competencies Ability to build systems where agents operate with independence
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Postdoctoral Fellow - Materials Chemistry, Texas Materials Institute, Cockrell School of Engineering
or parallel reactors Collaborate with computational scientists to integrate machine-learning models for closed-loop materials discovery Collaborate with companion postdocs on functional materials
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Lab researches on a variety of computer systems topics including HPC resilience, data center power management, large-scale job scheduling and performance tuning, parallel storage systems and scientific
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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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successful applicant will join Andrej Prsa’s research team and work on cutting-edge theoretical and observational aspects of binary and multiple stellar populations. In parallel, the applicant will teach