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are interested in candidates who have an interest in: *Advanced Manufacturing and Integration of Scalable Structures *Soft and Living Materials *Natural and Engineered Materials for Energy, Environment and
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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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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
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research levels in the areas of biochemistry, biophysics, cell biology, structural biology, microbiology, developmental biology, virology, genetics and cancer biology. The term of appointment is based
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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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genome-scale datasets, as well as proved expertise in their curation and analysis using state-of-the-art phylogenetics implementing phylodynamic models. Strong computational skills and programming
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required) Kinetic modeling proficiency (Chemkin, Cantera), analytical proficiency (sensitivity, rate of production, etc.) Spectroscopic modeling experience preferred (HITRAN/HITEMP) Familiarity with
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Dr. Bridgett vonHoldt is seeking to hire a postdoctoral associate (or other senior research) in the areas of evolutionary and ecological analyses of large genome datasets, modelling and simulation