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Field
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monitoring. Design and implement machine learning models to analyze multimodal data (e.g., student behavior, engagement, and performance) to enhance personalized learning. Develop and evaluate GPT-powered AI
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Responsibilities Minimum Qualifications Preferences The position is for a highly motivated individual with 0 to 2 years of postdoctoral experience. Candidates should hold a PhD and demonstrate a strong background in
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related field Desired Experience: 3 years of prior research experience on multiscale modeling, hybrid modeling, and model-based control or related fields. Additional Information: Location: CBEC (0248
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on cutting-edge research at the intersection of statistical modeling, artificial intelligence, and infectious disease epidemiology. This position focuses on developing and applying statistical, mathematical
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. A major focus will be on the identification of small molecules from mass spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available
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model fairness and model generalizability across multi-institutional electronic health records databases. The researcher will have access to the real-world EHR data from almost 20 sites across
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murine BMT models, and donor graft manipulation and pharmacological interventions. Candidates should have a PhD or MD and a strong foundation in cellular and/or tumor immunology. Candidates with a
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Qualifications Experience in electrophysiology and analytic methods for analyzing this type of data. Experience in computational neuroscience and modeling, as well as in the aging brain is desirable. FLSA Exempt
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. A major focus will be on the identification of small molecules from mass spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available
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data dissemination capabilities for making high-resolution earth system model output available to a diverse audience. Candidates must have a PhD in computer science, environmental and physical sciences