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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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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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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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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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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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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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assisting with managing the lab and projects. We also expect that you will collaborate with the ARG team on developing grant proposals. Qualifications Required qualifications: Doctoral degree in a related
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collaborative projects, work with and help mentor graduate students in our Program, participate in the regular SGS seminar series and other Program activities, and assist with relevant undergraduate or graduate
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are for one year with the possibility of renewal pending satisfactory performance and continued funding. This project is funded by the NSF award "Non-local magneto-curvature instabilities and their associated