10 algorithm-"Multiple"-"U"-"Simons-Foundation"-"Prof"-"UNIS"-"DIFFER" Fellowship positions in United States
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in artificial intelligence (AI) for settings involving multiple interacting decision-makers---whether autonomous AI agents, humans, or a combination of both. Applications include mixed-autonomy
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progress in machine learning and artificial intelligence, the successful candidate will have primary responsibility to develop, implement, and test multimodal machine learning algorithms to analyze and
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of approximately 1.7 million square feet and high-performance computing facilities at the DOD Supercomputing Research Center. This opportunity has multiple projects based out of the ERDC Field Research Facility in
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Together, these research directions seek to reimagine how buildings and cities operate—optimizing energy use, enhancing human well-being, and reducing carbon emissions at scale. We are seeking multiple
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at the intersection of neuroscience and AI, with opportunities for innovation and collaboration across multiple disciplines. Candidates are expected to have experience in cutting edge AI technologies and their
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analyses in nonclinical drug development. The postdoctoral role involves designing and implementing algorithms for anomaly detection, segmentation, and classification to contribute to the development
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algorithms. The candidate should have the following skills: • Strong technical, analytical, and quantitative abilities; • Strong interpersonal, organizational, and communication skills, and the willingness
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progress in machine learning and artificial intelligence, the successful candidate will have primary responsibility to develop, implement, and test multimodal machine learning algorithms to analyze and
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, prediction, and decision support and automation. Additional duties include but are not limited to: Developing and refining algorithms and workflows for crop monitoring, modeling, prediction, and decision
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research has been funded by multiple NIH R01 grants and industry grants with awards from AAPM and ASTRO. Dr. Ren is a Fellow of AAPM. Details about Dr. Ren’s profile can be found at the following link: https