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Field
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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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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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of belonging. The Postdoctoral Associate also will participate in weekly seminars with other Active Learning Initiative postdocs, receiving training and support in designing and implementing research-based
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Computer Engineering. Level Scope: After earning a Ph.D., the next step in the academic or research career path is often a postdoc. A postdoc is a continuation of a researcher’s training that enables them
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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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Using Machine Learning Algorithms (Plasmon-2Detect), ref. COMPETE2030-FEDER-00714300, number of the project - 16004, financed by the European Regional Development Fund (ERDF), with a view to the
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, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following link: https://resources.uta.edu/hr/services/records/compensation
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experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in particular simulation-based inference), strong programming
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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
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learning methods development and application. The postdoc associates will be exposed to rich multi-omics data, a variety of diseases, advanced statistical and machine learning methods and wide collaborations