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Field
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. Job description While automation offers opportunities to make society safer, it comes with new risks, some of which are fundamental and, others more technological. Autonomous agents require
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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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be leveraged to train AI-based tools in order to enhance resolution of low-field NMR spectra for these isotopes. Diagnosing metabolic disorders in the human brain by the development of new pulse
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, supply chain resilience, and lifecycle sustainability. The successful candidate will lead and conduct independent research in optimization, systems modeling, agent-based modeling (ABM), and network
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engineering or related fields. Candidates should have a strong research record in LLM-based agents, reinforcement learning, or large language models, preferably in areas closely aligned with the topics outlined
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), in France. Main tasks 1. A review of the social sciences literature on environmental assessment with a particular focus on energy infrastructures (including renewable energy production facilities). 2
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an exciting approach to agentic, fully autonomous thin film development using a combination of automated electroplating, in-operando measurements, and AI driven algorithms. He or she will work with a team of
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of computing and healthcare. Methodologies of interest include: Multi-modal learning Foundation models, including large language models Agentic AI Multi-agent AI systems Transfer learning Self-supervised
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interactions in physical environments over time can provide valuable insights of team dynamics. SWATE explores the creation of a Socially-Aware AI agent to enhance team training by providing real-time insights
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either or both agent-based modeling and social network analysis/network science. The ideal candidate should be able to program in C or C and have experience working with tera-bytes of data. Preference