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nano-plasmonics) design, fabrication, and characterizations. All candidates should have a Ph.D. degree. Appointments will be for one year, with the possibility of renewal pending satisfactory performance
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Theory for a position starting in September 2026. The researcher's duties will include making progress on their own research projects, teaching the equivalent of one course each year, and actively
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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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assigned by the Associate Director and Director Involved in other initiatives, projects, and activities, as assigned by the Associate Director and Director Performs other duties as assigned Qualifications
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will include conducting research, disseminating research results, collaborating on robotically fabricated structures, and assisting with managing the lab and projects. We also expect that you will
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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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interdisciplinary research projects within Princeton Precision Health. The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility of renewal pending
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the operation of a wide range of surface science methods, with emphasis on HRXPS, LEIS, AES, LEED, TPD, HREELS, Raman scattering, and SEM. Qualified candidates should possess experience for the design
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nano-plasmonics) design, fabrication, and characterizations. All candidates should have a Ph.D. degree. Appointments will be for one year, with the possibility of renewal pending satisfactory performance
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motivated and organized researcher with the following qualifications:Responsibilities*Explore, collect, and preprocess various sources to develop domain LLM training and test datasets*Design and implement