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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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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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addition to applicants’ prior accomplishments, we would like to learn what motivates them, how they approach unfamiliar problems, and what they hope to contribute to, and learn from, an interdisciplinary research
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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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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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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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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 7 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