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: Responsibilities *Explore, collect, and preprocess various sources to develop domain LLM training and test datasets *Design and implement fine tuning and RAG workflows for LLMs on a variety of datasets *Maintain
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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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. These models will be used to design and test policy and investment interventions to alleviate deployment bottlenecks. The successful candidate will have experience with applied energy systems analysis, economy
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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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Princeton University's Initiative for Data-Driven Social Science (DDSS) invites applications for Postdoctoral Research Associates. DDSS supports technical and methodological innovation in
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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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leverage these findings for bioengineering applications. Candidates completing (i.e., with a confirmed defense/viva date) or holding a PhD in chemical engineering, physics, bioengineering, chemistry, or a
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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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availability of funding. The anticipated start date for the position is June 1, 2025. Individuals with a strong theoretical background who expect to obtain a PhD in a related field (e.g., statistics
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data dissemination capabilities for making high-resolution earth system model output available to a diverse audience. Candidates must have a PhD in computer science, environmental and physical sciences