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Field
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Position Description UNIVERSITY OF SOUTH CAROLINA DEPARTMENT OF MATHEMATICS Postdoctoral Associates in Data Science & Machine Learning The Department of Mathematics at the University of South
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Experience Hands-on experience with mouse models and in vivo experiments Required License/Registration/Certification N/A Physical Requirements Check all that apply: ☒Sitting at computer work station for long
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 18 days ago
-series. Experience exploring machine learning and deep learning techniques for geospatial applications is highly desirable to effectively engage with Earth observation foundation models. Technical
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computing, high-performance computing (HPC), or machine learning (ML) Interest in Standard Model measurements and/or searches for new phenomena Application Review Review of applications will begin once the
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-augmented generation (RAG) approaches Systems and mathematical modeling of biological or complex systems Natural language processing and machine learning Data harmonization and integration Record of research
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, comparative genomics, machine learning, and evolutionary analysis to address fundamental questions in molecular biology and human disease. Responsibilities Develop computational pipelines for the discovery
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approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory grammar
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
connections between wet and dry lab, bringing together computational biologists building new methods at the interface of structural biology and machine learning and experimentalists studying how proteins
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, integrates, and evaluates innovative solutions in autonomous systems, including artificial intelligence (AI), machine learning, robotics, control systems, sensing technologies, and related disciplines
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models