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in Utah to recruit multiple postdoctoral fellows to apply high throughput methods and machine/deep learning to unlock the full potential of the dark proteome. Responsibilities Scientific visionRibosome
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
solid academic background in polymer synthesis and electrochemistry. The candidates should be self-motivated to explore new areas of studies including AI related and machine learning fields, have evidence
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 25-Mar-26 Location: Brooklyn, NY Categories: Academic/Faculty Internal Number: 183730 POSTDOCTORAL ASSOCIATE New York
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fellow to join our translational research program in macrophage biology/immunology. Our team takes a systems approach—integrating multi-omics, network science, machine learning, and comprehensive in vitro
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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, machine learning, and optimization, broadly defined. Applicants working at the intersection of these areas, especially those applying theoretical and computational methods to problems in management science
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postdoctoral fellowship at ENS Lyon in the field of machine learning. The position is part of the research project "Neural networks for homomorphic encryption", funded by Inria. Fully homomorphic encryption (FHE
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–functional modeling of root system architecture. Phenomics data integration and high-dimensional trait analysis. Predictive breeding and quantitative genetic modeling. Machine learning approaches to genotype
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to significantly extend our existing team’s capabilities for data scoring and analysis (e.g., with expertise in natural language processing, machine learning, or computational modeling). Finally, the
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materials using statistical mechanics, molecular simulations, and machine learning. Expectations Candidates will be responsible for: Developing multi-scale modeling methods for polymeric materials, using