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senior researcher in the areas of soft materials and polymer physics. The successful candidate will develop strategies to design, synthesize, and characterize the properties of soft materials using
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. The successful candidate will be expected to assist with the commissioning of a new shock tube facility and will conduct fundamental experimental research related to multiple ongoing projects, including
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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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, thermal management, and energy conversion. We seek candidates with strong expertise in building and conducting ultrafast time-resolved optical experiments. Key skills include the ability to design, assemble
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
for this position will work to develop a conservative machine-learning based sea ice model correction that can be applied to fully coupled climate model simulations. The project will involve: 1) the development of a
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their residency at Princeton to assisting with research and to their own work. Eligible candidate must have less than five years of post-PhD research experience prior to anticipated start date. This is a one-year
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earth system model data, with an emphasis on Seamless System for Prediction and EArth System Research (SPEAR) for seasonal to multidecadal prediction and projection. The project will emphasize elements
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researchers working on an NIH funded project focused on developing new systems models to examine social and biological drivers of infection inequality. The overarching goal of this postdoctoral position is to
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employed by another institution during the term of their Princeton appointment. Applications will be evaluated based on the applicant's previous accomplishments, the promise of the proposed research project
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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