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/user behavior, Collect and analyze human performance data (reaction times, workload, situational awareness, eye tracking, etc.), Develop and validate computational models of human behavior in driving
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of the research grant when extending an offer. The ideal candidate will hold a PhD in computational biology, biophysics, systems biology, bioinformatics, genetics/genomics, and/or plant biology, a track record or
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computational methods and collaboration, HELM addresses global challenges by blending eco-social health determinants, human-natural system interactions, and convergence research. The selected candidate will work
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and its applications, and is willing to learn new technologies. The candidate should have a PhD in Computer Science or a closely related field. Relevant background and skills include: Strong foundation
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neuroscientists pursuing advanced research and development in neuromorphic computing, artificial intelligence, and spiking neural networks across a range of applications. A strong background in theory (e.g
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-learning models (e.g., graph neural networks, equivariant architectures) in collaboration with computer science researchers. Applying developed models to problems in Earth and planetary interiors, such as
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Dr. Playdon’s research, visit her faculty profile https://profiles.faculty.utah.edu/u6017378 . Details about the NIH-funded project (1R01CA313949) can be found on NIH RePORTER: https://reporter.nih.gov
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The successful candidate will have completed a PhD in chemistry education or related field (ABD will be considered), and will be performing research in line with the PI's research program. The postdoctoral scholar
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. The candidate is expected to have a strong background in developing innovative electronic hardware and computational tools for biomedical research and to work effectively within a multidisciplinary research
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in probabilistic temporal event dynamics. Required Qualifications: - PhD in computer science, engineering, biomedical data science, informatics with advantage for experience in conducting research