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background in chemical and biological engineering, bio-engineering, molecular biology, microbiology, biochemistry, biophysics, computational modeling or related fields. Experience in metabolic engineering
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to ion beams with well-controlled energies and incident angles for benchmarking and validation of theoretical calculations and computational physics and chemistry modeling of important surface processes
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on data science and engineering. The scientist will collaborate with Princeton and GFDL researchers to enhance, analyze and deliver high-resolution earth system model data, with an emphasis on Seamless
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advance regenerative medicine. For more information about the lab, please visit https://mesa-lab.org/ .Projects will utilize in vivo mouse models, transcriptomic techniques, and advanced intravital imaging
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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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computational modeling techniques to study planning in rodents engaged in dynamic spatial foraging tasks. The successful candidate will develop computational models of reinforcement learning in the brain and
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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
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Caggiano at the University of British Columbia as well as multiple community partners. The position will be based at the Andlinger Center for Energy and the Environment, which is focused on resolving
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advance regenerative medicine. For more information about the lab, please visit https://mesa-lab.org/. Projects will utilize in vivo mouse models, transcriptomic techniques, and advanced intravital imaging
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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