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further develop research software, such as simulation environments, data pipelines and infrastructure, for the training and evaluation of machine learning models within distributed AI. Contribute to and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
motivated candidate with a strong interest in machine learning and clinical neuroscience. The lab studies resilience and vulnerability in brain aging and dementia, and builds computer-aided methods and tools
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About the Role We are seeking a Postdoctoral Research Associate in Machine Learning for Cardiovascular Digital Twins to join a team working on the Precision Health, Cardiovascular Devices and Trials
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through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to advance
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 23 hours ago
. Examples include: determination of high-resolution shape models using shape-from-shading or novel machine-learning methods; studies of high-resolution shape models for geophysical and geological studies
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Experience developing software with Python, R, and/or Julia Experience with machine learning and AI algorithms and tooling (e.g. PyTorch) Experience with remote sensing and open-source GIS tools (e.g. Google
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analyse electrochemical free-energy landscapes and connect elementary-step energetics to catalytic performance apply machine-learning interatomic potentials and microkinetic models where appropriate
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Science, Artificial Intelligence, or a related discipline. You will have experience in BIM and digital construction, digital twins, AI and machine learning, cloud computing, IoT integration, or data
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discoveries made in the laboratory with meaningful improvements in patient care. You will be part of a research environment that values curiosity, creativity, scientific rigor, and continuous learning while
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background in Control Systems Theory (e.g., MIMO control, state-space methods, optimal control, model predictive control, system identification) AND/OR Artificial Intelligence / Machine Learning / Data Science