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collaborative research environment and will work closely with investigators across psychiatry, neuroscience, engineering, data science, and neuroimaging methods. The position provides opportunities to collaborate
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responsible research practices. Minimum Qualifications: A doctoral degree, such as PhD, MD, MD/PhD, or equivalent, in Biomedical Engineering, Neurobiology, Neuroscience, Cell Biology, Molecular Biology, or a
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innovation in autonomous scientific discovery. • Science of AI: We investigate the theoretical foundations of machine learning itself, leveraging tools from quantum information, statistical mechanics, and
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distinguished jury on the final day. Successful candidates will be offered a position at the BioMed X Institute in New Haven. About BioMed X BioMed X is a leading innovation engine where disruptive ideas in
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date
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to support a two-year Small Business Technology Transfer project (STTR) funded by the National Science Foundation. The successful candidate is expected to support the development, simulation and
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for persistent pain in humans. CNAP is a research Center of Excellence inaugurated in 2015 by the Danish National Research Foundation at Aalborg University, Denmark (www.cnap.hst.aau.dk ). CNAP applies a basic
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Postdoctoral Associate in Health Systems Engineering Cornell University’s Center for Transportation, Environment, and Community Health (CTECH)—a U.S. Department of Transportation Tier 1 University
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forward to completion. You will act as the intellectual engine for our mechanistic immunology studies. Advanced Execution: Design and execute highly complex, fundamental basic science and translational
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 14 days ago
assimilation Integration (JEDI) framework for atmospheric data assimilation Develop machine learning foundation models based on simulated observations from high fidelity atmospheric simulations for developing