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that challenge prevailing assumptions, employ cutting-edge technologies, or integrate machine learning with neurobiological data are especially welcomed. Projects focusing primarily on animal models with
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Learning for Foundation Models’, where the aim is to adapt these models to new tasks without forgetting previous knowledge. The precise focus of the project can be defined in collaboration with
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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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ML models and Python programming. Work Objectives: The main objective of this position is to develop, implement, and validate advanced machine learning methodologies within the scope of the project
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education and economic development needs. Federally designated as a senior military college, one of the university's signature leadership programs is its 800-member Corps of Cadets on UNG's Dahlonega Campus
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assimilation, machine learning, and seasonal weather forecasts. As a Postdoctoral Research Fellow, you will play a crucial role in developing and testing statistical models for the accurate forecasting
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(SEADS) directorate. The SEADS directorate contributes to an exciting portfolio of energy analysis and grid futures studies, applying advanced modeling and simulation tools to optimally plan, design, and
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research in hydrogeophysics, including areas such as field-based geophysical observations, computational modeling, and AI-driven analysis. Essential FunctionsEmpty heading Teach undergraduate and graduate
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University of California Agriculture and Natural Resources | Davis, California | United States | 2 days 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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implement new nonlinear iterative solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned