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. The candidate should have a PhD in Computer Science or a closely related field. Relevant background and skills include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval
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findings, and help attract funding. PhD in Computer Science, Computational Bioengineering, Mechanical or Electrical Engineering Excellent coding skills Preferably a familiarity with machine learning and deep
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contribute to patents or technical innovations. Qualifications: PhD in Artificial Intelligence, Machine Learning, Data Science, Electrical Engineering, or a related field. Strong experience in developing and
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 12 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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experience at a synchrotron radiation beamline hands-on experience in X‑ray microscopy techniques, X‑ray diffraction or X‑ray fluorescence working knowledge of image processing e.g. using machine learning
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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requirements, including link budgets, beam steering, and orbital pointing dynamics. • Experience with optimization methods and physics-informed machine learning. • A strong publication record in antennas
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trials to multi-omics explorations, imaging, biomarker development, stable-isotope tracers, and large-scale data set s and machine learning. The lab is now recruiting for a postdoctoral researcher, who
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collaborate with other technical groups working on the design. The successful candidate will also have opportunity to conduct experiments and machine development activities on the existing accelerators. The key
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of educational data science, AI in education, and the learning sciences, with additional advisory support from faculty and researchers across learning sciences, computer science, machine learning, and education