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Field
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physical laws, or an implicit form of extra data examples collected from physical simulations or their ML surrogates. In medical domains, patient data is typically distributed across multiple hospitals
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Responsibilities: Conduct research on the design and analysis of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop algorithms and prototypes
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of fairness, safety, and privacy in digital technologies. Key Responsibilities: Conduct research and development in trust technologies – translating algorithms into tools and frameworks and implementing working
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the next generation of distributed software infrastructure for JGI, represent the JGI in relevant reviews, national and international activities, and within the broader DOE National Laboratory system
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modelling and simulation of transmission and distribution networks, including benchmarking data models, developing optimal power flow algorithms, and creating state estimation and multi-energy optimisation
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grow the computer science and distributed infrastructure aspects underpinning AI4Science at scale. In addition, the incumbent will be mentored and work with CSD Leadership, participate in building core
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of prior data?) Additional research topics may include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success
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, financial networks, e-democracy, voting, social networks, online analysis with delay, and theory of distributed algorithms. In our group, we work on both theory and practice: some members of our group focus
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computational tools to support the safe and ethical deployment of AI in clinical settings. The research focus is on AI performance monitoring, distribution shift detection, bias assessment, and stress testing
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Apply Now Job Summary The Student Research Assistant will support a faculty-led research project examining how algorithmic bias affects social equity in areas such as healthcare, hiring, and