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research backgrounds in game theory, mechanism design, artificial intelligence, machine learning, and optimization, broadly defined. Applicants working at the intersection of these areas, especially those
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-changing technologies. Life-changing careers. Learn more about Sandia at: https://www.sandia.gov *These benefits vary by job classification. What Your Job Will Be Like: We are seeking a Postdoctoral
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
solid academic background in polymer synthesis and electrochemistry. The candidates should be self-motivated to explore new areas of studies including AI related and machine learning fields, have evidence
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 25-Mar-26 Location: Brooklyn, NY Categories: Academic/Faculty Internal Number: 183730 POSTDOCTORAL ASSOCIATE New York
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, machine learning, and optimization, broadly defined. Applicants working at the intersection of these areas, especially those applying theoretical and computational methods to problems in management science
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programming, large-scale optimization, and simulation-based optimization; and/or Expertise 2: Experience with general methods in artificial intelligence and machine learning, including reinforcement learning
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in Utah to recruit multiple postdoctoral fellows to apply high throughput methods and machine/deep learning to unlock the full potential of the dark proteome. Responsibilities Scientific visionRibosome
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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materials using statistical mechanics, molecular simulations, and machine learning. Expectations Candidates will be responsible for: Developing multi-scale modeling methods for polymeric materials, using
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using liquid biopsy next generation sequencing data for cancer diagnostics. About You Must have a strong background in next generation sequencing data analysis/machine learning, cancer and/or genome