70 composite-residual-stress-development Postdoctoral positions at Princeton University
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: (i) measurements of surface composition, structure, and thermal stability using AES, LEIS, XPS, and vibrational spectroscopy; (ii) quantitative determination of the flux and fluence of incident atoms
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ultrahigh vacuum (UHV) surface science facilities enable: (i) measurements of surface composition, structure, and thermal stability using AES, LEIS, XPS, and vibrational spectroscopy; (ii) quantitative
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ultrahigh vacuum (UHV) surface science facilities enable: (i) measurements of surface composition, structure, and thermal stability using AES, LEIS, XPS, and vibrational spectroscopy; (ii) quantitative
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Branch is dedicated to accelerating the study of metabolic phenomena associated with cancer to develop new paradigms for cancer prevention and treatment. Its main research areas include: - Metabolic
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*Strong publication record (relative to degree timing) *Collaborative spirit in interacting with postdoctoral and PhD researchers on the team *Interest in developing and applying Large Language Models (LLM
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scientists for research and development activities focused on data science and engineering. The scientist will collaborate with Princeton and GFDL researchers to enhance, analyze and deliver high-resolution
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project. The ideal candidate will have experience in yeast strain development and engineering CRISPR-based control of gene expression. This position will allow for both professional and laboratory skill
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of whole genomic data already available; and ii) investigating rates of evolution across the genome and their correlation with phenotypic traits across various subdivisions of the squamate body plan
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; biodiversity; conservation; environmental science and policy; infectious disease and global health; and sustainable development in impoverished and resource-challenged regions of the world. The Term
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starting July 2025, and will remain open until excellent fits are found. The successful candidate will develop and apply computational approaches to chemical datasets, with artificial intelligence/machine