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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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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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year with the possibility of renewal pending satisfactory performance and continued funding.About UsARG is an interdisciplinary laboratory for advanced research at the intersection of design, computation
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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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://pritykinlab.princeton.edu) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi-modal single-cell, spatial and genome editing
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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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. 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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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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include designing online studies for peer interaction with children, writing manuscripts, presenting research, and mentoring undergraduate and graduate researchers in the group. The term of appointment is
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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