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knowledge-grounded reasoning with flexible machine learning Tools that reduce manual burden while preserving traceability and clinical interpretability This position offers the opportunity to publish novel
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, pharmacology, genomics and multi-omics, as well as growing methods in advanced analytics of health data e.g. machine learning to improve human health with a focus on therapeutics. These posts will work alongside
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of the faculty are expected to lead research programs that will produce significant contributions to a field, and teach and mentor at the undergraduate and graduate levels. This position is benefits-eligible
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 18 hours ago
for the postdoctoral position based on official transcripts and other documentation as required. If the candidate(s) will teach during their appointment, then additional teaching credentials are required. UNC Wilmington
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, such as statistical learning, causal inference, experimental design, probability, stochastic PDEs, algebraic statistics, normalizing flows, scientific machine learning, optimization, inverse problems
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or Associate Professor level in the field of statistics or machine learning, with demonstrated interest in single-cell or spatial genomics applications. This faculty member will have a unique opportunity to
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at the interface of machine learning, data science, and the chemical sciences to lead the AI for the Chemical Sciences Summer Bootcamp. This individual will design and deliver an intensive, hands-on educational
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convection. The objective is to develop reduced but realistic models of cumulus life cycles that may be applied toward cumulus parameterization and/or machine-learning algorithms for predicting short-term
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or machine learning, or related discipline required by the time the appointment begins. Additional Qualifications Demonstrated strong commitment to teaching and advising is desired. Special
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. Position Overview The successful candidate will develop and apply advanced computational and machine learning methods to large-scale genomic, clinical, and imaging datasets, working across one or more of the