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project studying the neurocomputational basis of reinforcement learning in rodents. The project, in collaboration with the Berke and Frank labs at UCSF, combines advanced system neuroscience and
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health services to Princeton University faculty, staff, and employees. An integrated, evidence-informed model guides all UHS practices and services. UHS leverages clinical encounters and prevention efforts
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information about the lab, please visit https://mesa-lab.org/. Projects will utilize in vivo mouse models, transcriptomic techniques, and advanced intravital imaging to investigate: 1) How immune cells localize
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optimization (theory, modeling, and tools). Candidates should apply at: https://www.princeton.edu/acad-positions/position/39361 and include a cover letter, CV (including a list of publications), research
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, including but not limited to cement chemistry, material science, or structural materials and mechanics. Candidates with a strong commitment to interdisciplinary research are especially encouraged to apply
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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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research problems *Ability to work independently and to adjust to rapidly changing needs of researchers Preferred Qualifications: *Experience with macroeconomic or structural modeling. Specific experience
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maintaining a shock tube facility (operational proficiency required)Kinetic modeling proficiency (Chemkin, Cantera), analytical proficiency (sensitivity, rate of production, etc.)Spectroscopic modeling
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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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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion