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to conduct research on developing and using machine learned parameterizations developed from ocean-data assimilation increments. The goal is to develop parameterizations of unresolved processes that will
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literal contributions to the scientific community. CORE JOB FUNCTIONS Develops and implements advanced computational and machine learning methods for the analysis of large-scale omics data, including
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have experience expertise in one or more of the following: MRI pulse sequence design (GE HealthCare EPIC Preferred) Programming in Python and C/C++ Machine learning with PyTorch Excellent writing skills
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analysis, probabilistic modeling for deterioration processes, and decision-making under uncertainties is highly desirable. Experience in applying machine learning and performing Bayesian updating is highly
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may be desired; experience in phlebotomy may be desired; experience performing basic laboratory procedures to vital signs may be desired; computer skill required with knowledge of database software
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-level understanding of kinetics in solid-solid phase transitions; Develop advanced machine learning methods for the fast prediction of materials properties; Publish results in peer-reviewed journals and
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experience in designing and implementing climate model experiments are particularly encouraged to apply. Expertise in computer programming and high-performance computing is needed. A demonstrated ability to
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accordingly to research specifications. Uses a computer terminal to input and retrieve data and to generate reports. 3. Processes and summarizes data using scientific or statistical techniques. Assists in data
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environment, you’ll 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
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), coastal hazard assessment, model coupling, model calibration, and validation. Experience with machine learning and statistical/probabilistic analysis. Strong programming/scripting capabilities and high