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Field
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new NIH-funded Center for Excellence in Multiscale Immune Systems Modeling . This position focuses on the development, calibration, and analysis of multiscale agent-based models (ABMs) and differential
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sampling; selection, acquiring, reformatting, and sampling gridded environmental data for use as model covariates; creating species density surface models, typically in R with mgcv-based generalized additive
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. You will build simulation agents that predict materials properties (such as Li-ion conductivity in solid electrolytes) autonomously and at scale, validate them against thousands of real A-Lab
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fields: Agentic AI, Large Language Models, Artificial Intelligence, Biomedical Ontologies, Biomedical Knowledge Graphs, Computational Biology, Bioinformatics, Biomedical Informatics or a related field
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the distributed hydrologic model ATS (The Advanced Terrestrial Simulator), coupled to a trait-based plant-soil-microbe model (ATS-EcoSIM). The code provides a framework for three-dimensional hydrologic
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top-tier venues and will have dedicated time for advancing their own research. The project is in collaboration with CCL (The Center for Connected Learning and Computer-based Modeling, PI Uri Wilensky
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deploying an AI project. This includes initial problem specification, data gathering and analysis, model creation, and implementation. Applicants should be willing to learn and use agentic AI to build and
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, qualification, and deployment of AI agents and models, Computational Fluid Dynamics (CFD) simulation codes, and Finite Element Method (FEM) based tools for nuclear energy (fission and fusion) applications
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deploying an AI project. This includes initial problem specification, data gathering and analysis, model creation, and implementation. Applicants should be willing to learn and use agentic AI to build and
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of electrochemistry and artificial intelligence. Ideal candidates will have experience in machine learning, large language models, AI-agent development and computational workflows, with particular interest in building