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aims at developing a new and innovative system for the large scale analysis of stellar spectroscopic data. The "CHEmical Survey analysis System" (CHESS) will combine machine learning ideas with
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engineering, computer science, or machine learning. The title will be specified according to the qualifications and merits of the selected person. The following will be considered an advantage: Good skills in
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complete) a PhD in Physics/Astrophysics with focus on data analysis, preferably of gravitational waves data · You have experience and in-depth understanding of machine learning methods for data
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on topics including molecular clouds and star formation. Candidates with experience in machine-learning and magnetic field observation/simulation are particularly welcome to apply. Experiences with (sub-)mm
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and renewable energy Artificial intelligence applied to social sciences - International Economy Artificial Intelligence and Machine Learning Work on High Frequency circuits, High Frequency Sensors
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require knowledge of both languages and you must be happy learn the other language if you are not experienced. We are not expecting you to be an expert in all forms of computer simulation and web deployment
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QCD data from the STAR experiment. We have recently collected significant data sets with polarized high-energy proton collisions and expect to continue to collect more in the RHIC Run-24 with a wide
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analysis, dynamical systems and/or discrete mathematics; the ability and willingness to collaborate with mathematicians with complementary expertise; Computer simulations and statistical mechanics play a
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., R, Matlab), and high performance computing. Applicants with experience in physical oceanography, machine learning, Bayesian statistics, and/or data assimilation are preferred. The incumbent must have
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have the opportunity to engage through our ten employee resource groups, numerous employee-driven clubs, and learning and professional development classes. NREL supports inclusive, diverse, and unbiased