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candidates may include: 1.Energy modelers with expertise in integrated assessment modeling, energy system modeling, or agent-based modeling, who are eager to incorporate socio-political dynamics
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candidates may include: 1.Energy modelers with expertise in integrated assessment modeling, energy system modeling, or agent-based modeling, who are eager to incorporate socio-political dynamics
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-based models as well as patient-derived xenograft models of liver cancer. This position is suitable for a highly motivated self-starter who excels in a dynamic environment offering varied learning
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supervision of PI Calina Copos and will engage in collaborative work with cell and developmental biologists. Expertise in agent-based models, continuum PDE descriptions, dynamical systems, and/or ML-based
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. The preferred candidate will have a strong academic or industrial background in machine learning, trustworthy machine learning and AI, agentic AI, adversarial machine learning, graph-based learning, multi-domain
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assessment of lymphatic flow, 2) Advancing methods for contrast-enhanced MRL, 3) Creating ideal contrast agents for MRL, and 4) generating phantom and animal models for MRL optimization and validation
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intensive multi-cellular agent based models (ABMs) of Epstein–Barr Virus (EBV) and HIV-1 infection dynamics in human lymphoid tissue. This postdoctoral associate will collaborate with experimentalists
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for Risk Forecasting, Large Language Models (LLMs), and Human-in-the-Loop AI Systems. Our aim is to advance AI for Operations by integrating next-generation AI agents and LLMs with real-world operational
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applications for biomedical research. The candidate’s work will focus on developing AI methods, training AI models, and creating agentic AI workflows on DOE supercomputers and applying them to population-level
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) to train and direct support personnel. The candidates are expected to have experience in animal bone marrow transplant models, DNA repair pathway, flow cytometry, and lentiviral-based gene transfer. Prior