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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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for this position is $62,500.00- $65,000.00. New York University considers factors such as (but not limited to) the specific grant funding and the terms of the research grant when extending an offer. PhD For people
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, reinforcement learning, probabilistic modeling, and language-guided autonomy. Core Responsibilities: Conduct independent and collaborative research aligned with the themes above Mentor PhD and MS students Lead
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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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and great opportunity of interdisciplinary training in machine learning and functional genomics. The project combines cutting-edge computational approaches, especially state-of-the-art machine learning
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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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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 24 minutes ago
analysis software packages. Demonstrated success in collaborative environments. Experience with one or several of the following: machine learning, data assimilation, ArcGIS, NASA satellite datasets. Point
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Qualifications PhD Certificates/Credentials/Licenses PhD Computer Skills Microsoft Office Supervisory Responsibilities No Required operation of university owned vehicles No Does this position require direct animal
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Requirements Applicants must hold a PhD degree in Machine Learning, Artificial Intelligence, Computer Science, Statistics, or a closely related field. A strong research background and programming experience
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lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP