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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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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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modelling of transport in WDM and compare predictions against experimental data. For both positions, we expect Machine Learning techniques to be used/deployed in the data analysis and in the creation
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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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specifically, we are developing Deep Learning tools to detect galaxy edges or truncations (i.e. a physically-motivated size indicator based on a sudden decrement of the number of stars in the galaxy outer parts
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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
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supportive, inclusive, and flexible work environment, you’ll have the opportunity to engage through our eight employee resource groups, numerous employee-driven clubs, and learning and professional development
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condensers Support integrated multi-physics simulations of reactor core, and other system components Develop reduced-order calibration approaches and apply machine learning and Bayesian calibration methods