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website: https://cruchagalab.wustl.edu/ . Research Projects: Plasma, CSF and Brain Proteomic analysis. Biomarker identification through the use of machine learning and AI approaches. Integration
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
machine learning methods like machine learning interatomic potentials is a preferred qualification. This preferred start date is January 2026 and can be negotiated. The initial appointment is for one year
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research on how engineers learn, how engineering knowledge and identities are formed, how we assess and evaluate learning, and how educational systems can be designed and transformed to support meaningful
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simulation models used in rare-event searches, develop machine learning methods for background rejection, and place constraints on new physics using LZ datasets. In addition, the postdoc will lead the
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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workforce equipped with expertise in integrating advances in biomedical engineering, technology, and Artificial Intelligence (AI) and Machine Learning (ML) methods to tackle complex biomedical challenges in
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completed until an initial determination of qualification for employment has been made. Union Representation: This position is represented exclusively by Postdocs United/United Automobile, Aerospace and
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a novel multi-omics approach that integrates high-throughput imaging and machine learning methods with CRISPR/Cas9 screens and saturation mutagenesis to answer central questions about the
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the Research Triangle, an affordable, vibrant and growing community. Learn more here: https://research.duke.edu/prospective-postdocs/. Job Code: 00003820 POSTDOCTORAL ASSOCIATE Job Level: 00 Duke is an Equal
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hardware Experience with atomic layer deposition and process development Experience with thin film and materials characterization Strong background in computational materials science and machine learning