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working on diverse scientific and security problems of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) development of novel machine learning models and
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standards and processes. You’ll use your expertise in ETL concepts, REST/SOAP APIs, XSLT, authentication, UML diagramming, and object data models to create, monitor, maintain, diagnose, troubleshoot, and
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intelligence, statistical learning, data mining, machine learning, database fundamentals, and related data models and queries. The IAD is the home of several graduate programs, including a Ph.D. in Computational
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including: Multi-scale Modeling / Computational Biologist / Bioinformatician – Research Scientist Artificial Intelligence (AI) / Machine Learning (ML) – Research Scientist Experimental Immunologist – Research
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, Associate, or Full Professor whose research and teaching advance core AI and ML theory, foundation models and generative AI, reinforcement learning and autonomous agents, hardware and systems for AI, edge and
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machine learning Data analysis and advanced statistics Economic and social transformations related to digitization Experince when it comes to programming (preferably Phyton) and in the use of modern tools
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market using data available on online recruiting platforms, deploying state-of-the-art approaches in Natural Language Processing, Semantic Web, and Agent-based Modeling. For this purpose, an extensive
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, ranging from antibacterial to anti-cancer drugs. To this end, we develop new ways to combine state-of-the-art technologies in metabolomics with mathematical modeling. The candidate will have the opportunity
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University of California Agriculture and Natural Resources | Davis, California | United States | about 12 hours ago
Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 13-Jan-26 Location: Davis, California Type: Full-time Categories: Academic/Faculty Computer/Information Sciences
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comfort throughout the year in a Nordic climate? Is it possible to predict dynamic outdoor thermal comfort with sufficient accuracy using fast parametric algorithms and machine learning (ML) models instead