530 computer-engineering-"https:" "https:" "https:" Postdoctoral positions in United States
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Field
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). The training program is designed to train the next generation of scientists and build a workforce equipped with expertise in integrating advances in biomedical engineering, technology, and Artificial
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/or peer-reviewed journals Required Knowledge, Skills, and Abilities: PhD in Computational Physics, Chemistry, Materials Science, Computer Science/Engineering, Applied Mathematics, or a related field
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existing and new computational methods to study multiscale processes in catalysis and photochemistry. Other projects that align with our general interest are also possible. Please see https://sdonglab.org
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. Qualifications Qualifications: PhD in Civil & Environmental Engineering, Hydrology, Geosciences, or a related field. Strong background in laboratory experimentation (microfluidics involving microbes, reactive
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Science, Electrical/Computer Engineering, or a related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with
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, computer science, electrical engineering, applied mathematics, or operations research) before May 2025 are encouraged to apply. Ideal candidates will display outstanding ability for research and a record of
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, computer science, physics, or engineering by the start date of employment. Strong expertise in one or more of the fields of scientific computing, data science, and quantum computing accompanied by strong programming
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Qualifications Ph.D. in Engineering, Physics, Ecology and Forestry, Earth Science, Computational Science, or related fields. Ph.D. research experience in wildfire engineering, fire science, landscape modeling
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Associate I, Information Science Posting Number req25315 Department Information Science Department Website Link https://infosci.arizona.edu/ Location Tucson Campus Address Tucson, AZ USA Position Highlights
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-sensory-guided behavior. The lab uses animal behavior, optogenetics, multi-area in vivo electrophysiology, and computational modeling tools to study information flow between cortical and subcortical brain