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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 hours ago
to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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://www.academictransfer.com/en/jobs/357341/postdoc-position-on-federatedco… Requirements Specific Requirements We are looking for a researcher who sits at the intersection of Pervasive/Mobile Computing and Machine Learning
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due
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: Support the development of AI and machine learning algorithms for autonomous navigation. Assist in building digital twin models to monitor drone health and mission performance. Contribute to IoT integration
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familiarity with applied science frameworks such as the Global Coral Reef Monitoring Network (GCRMN) or the IUCN Red List of Ecosystems. If you have any questions, please email Kelly Bradham at Kelly.bradham
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: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics
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motivated post-doctoral associate with a strong background in control systems and machine learning to join the research team of Prof. M. Umar B. Niazi. The position focuses on the development of digital twins
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in working with machine learning for batteries, with
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agricultural science with a quantitative focus (or an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and