109 phd-scholarship-for-solid-mehanical-engineering-in-image-processing Postdoctoral positions at Princeton University
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engineering pipeline, developing usable and innovative solutions to build a prototype of sustainable online tools and services that are relevant and useful to a broad range of stakeholders making decisions
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senior ranks may have multi-year appointments. A PhD is required, with appropriate research experience in quantitative biology, (bio)physics, (bio)engineering or related Engineering and Physical sciences
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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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, materials science and engineering, chemical engineering, or in a relevant engineering field, with an extensive background and training in the operation of a wide range of spectroscopic and imaging techniques
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be carried out independently or in collaboration with members of the Geosciences Department. One or more Hess Fellows may be appointed. Applicants must have or be in the process of completing a Ph.D
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researchers at Princeton and other institutions, to study novel renewable energy technologies. The candidates are expected to have a PhD degree in Chemical Engineering or related field, and have experience with
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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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, with appropriate research experience in quantitative biology, (bio)physics, (bio)engineering or related Engineering and Physical sciences disciplines, and a solid publication record. We seek faculty
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, materials science and engineering, chemical engineering, or in a relevant engineering field, with an extensive background and training in the operation of a wide range of spectroscopic and imaging techniques
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genomic data for reconstructing evolutionary patterns and processes that have shaped biological history across deep timescales. The ideal candidate will have a background in phylogenomics and bioinformatics