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a broad community. The scope of the work builds on recent publications from the laboratory, e.g. integrating language models with mass spectrometry data (https://www.nature.com/articles/s42256-021
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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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, lipid vesicles, polymer physics, active materials, single molecule biophysics, biomaterials, materials chemistry, fluid mechanics, rheology, and computational modeling. Candidates should apply at https
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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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must apply online at https://www.princeton.edu/acad-positions/position/37941 and via email to Sabine Kastner (skastner@princeton.edu ) and should post all application materials (cover letter, CV
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skillsExpertise in Generative AI: Strong background in machine learning, with specific experience in Large Language Models (LLMs), and Vision-Language Models (VLMs)Excellent programming skills (Python is required
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The Rosen Research Group at Princeton University (https://rosen.cbe.princeton.edu) is searching for a postdoctoral or more senior researcher interested in computational materials design and
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, single molecule biophysics, biomaterials, materials chemistry, fluid mechanics, rheology, and computational modeling. Candidates should apply at https://puwebp.princeton.edu/AcadHire/position/38901 and
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University faculty, staff, and employees. An integrated, evidence-informed model guides all UHS practices and services. UHS leverages clinical encounters and prevention efforts into meaningful opportunities
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incident angles for benchmarking and validation of theoretical calculations and computational physics and chemistry modeling of important surface processes occurring at plasma-material interfaces in fusion