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world and a suite of associated machine learning tools. The incumbent will be advised by Dr. Laurel Symes (CAPS, [email protected]). Depending on the research direction, collaboration and additional
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Science, or a closely related field. Strong theoretical background in machine learning with proven experience in applying reinforcement learning to practical, applied problems. A clear interest in human health
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learning (RL). 2) The Automation Group: Experts in designing and deploying custom robotic systems engineered for high-throughput sample synthesis, processing, and analytical characterization. This role is
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stimulation (TMS), and transcranial direct current stimulation (tDCS) through interdisciplinary collaborations. Behavioral measurements and physiological recordings, including facilities for computer-based
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Saint Louis University is a Catholic Jesuit Institution dedicated to student learning, research, health care and service. This Postdoctoral Fellow position is in the medicinal chemistry research group led
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. Knowledge of survival analysis. Knowledge of machine learning methods. Other Requirements Application materials must include: Curriculum Vitae (CV) Statement of research interests Names of three professional
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learning and benthic systems. This position offers an opportunity to contribute to cutting-edge research, particularly advancing the use of remote sensing datasets and machine learning for constructing
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will lead and participate in observations and data analysis across the electromagnetic spectrum, or will lead work on machine learning classification of optical transients. Applicants with previous
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Informatics (HOBI) at the University of Florida (UF) is seeking a highly motivated Postdoctoral Researcher with a strong background in biomedical informatics, artificial intelligence, machine learning, or data
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 6 days ago
); and propagation of physics-traceable soil moisture uncertainty into precipitation estimates. Machine learning approaches are welcome as tools within this framework — for example, learning the space–time