60 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "FORTH" research jobs at Oak Ridge National Laboratory
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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optical systems, thermal imaging, pyrometry, spectroscopy, high speed imaging or acoustic sensing. Familiarity with data analytics, machine learning, or signal processing. Knowledge of metal additive
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the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment. Once
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work to exciting research in multi-disciplinary domains alongside globally recognized experts. You will bring creative thinking, teamwork, and machine learning skills to bear as you develop new methods
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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funded by the U.S. Department of Energy (DOE) Office of Basic Energy Sciences (BES) in the Materials Sciences and Technology Division (MSTD). The successful candidate will be expected to work effectively
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laboratory investigations with inorganic mercury and methylmercury. Acquire and analyze data using a range of analytical instrumentation. Maintain detailed and accurate records. Prepare oral and written
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Expertise in machine learning and big data analysis Excellent written and oral communication skills Motivated self-starter with the ability to work independently and to participate creatively in collaborative
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candidates: If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk