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The Pena lab primarily studies the molecular mechanisms of brain development and the impact of stress across the lifespan, using a mouse model. This position will support the lab generally through
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. A major focus will be on the identification of small molecules from mass spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available
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Seeking a temporary Research Associate in machine learning. Pursue research in machine learning. Work on new forms of diffusion-based generative models. Meet with postdocs Participate in group
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research, and follow developments in the fast-changing field of AI, including but not limited to, familiarity and technical understanding of AI models and their uses and developments. This is a full-time
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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science and learning, high-performance computing, multiscale integrated modeling, and software technology. While our current strengths reflect the traditional focus of the Laboratory on magnetic confinement
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mathematics, data science and learning, high-performance computing, multiscale integrated modeling, and software technology. While our current strengths reflect the traditional focus of the Laboratory
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simulations, statistical mechanics, computer programming (e.g., C++, Python), polymer theory, molecular modeling (e.g., of proteins, nucleic acids, ligands), coarse-grain and polymer model development
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. This Initiative pursues research on the study, design, and evaluation of large AI models (especially language models); their applications to various academic disciplines on campus; and study of their impacts
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of laminar/neuropixel probes and electrical microstimulation to study attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH