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) with computational methods. The candidate will obtain single-molecule multiplexing data and validate machine learning predictions using the high-throughput data. The successful candidate will collaborate
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to teach the hard sciences in schools. Eligibility: Must be a rising sophomore, junior or senior; Must be majoring in the hard sciences OR be training to teach the hard sciences in schools. (Hard sciences
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advanced analytical approaches, including deep learning and machine learning, to improve disease subtyping and risk prediction. You should have a strong willingness to learn, enjoy tackling challenging
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Are you fascinated by how curiosity shapes learning in the classroom? Would you like to investigate how children seek information, explore and learn in real-world educational settings? If so, then
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ability to quickly learn and apply new software tools; experience with database applications and internet navigation required. Effective oral and written communication skills with the ability to clearly
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and you may be eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please
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analysis. • Experience with, or a strong interest in, machine learning, probabilistic modelling, statistics, signal processing, digital health or wearable sensor technologies. • Programming
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3.0 GPA Award Varies Scopes Alumni Affairs Deadline 02/26/2027 Supplemental Questions Tell us about an accomplishment or success that is not academic-related, and what you learned from it.
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of Teaching & Learning Deadline 02/26/2027
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, and clinical data Develop and apply computational, statistical, and machine learning methods for biomarker discovery and risk prediction Investigate molecular mechanisms linking immune aging