32 digital-image-processing-phd-scholarship Postdoctoral positions at The University of Arizona
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here . Duties & Responsibilities Organize existing data from measurement campaigns in 2024 at the LEO; these data include water vapor and CO2 flux measurements using LiCOR, thermal infrared images
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here . Duties & Responsibilities Organize existing data from measurement campaigns in 2024 at the LEO; these data include water vapor and CO2 flux measurements using LiCOR, thermal infrared images
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(depending on experience) to contribute to ongoing research in imaging science for polarimetry. The successful candidate will work under the supervision of Meredith Kupinski and collaborate with a
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, presentation of results at conferences, scientific mentoring of junior colleagues, and preparation of original scientific research manuscripts. Minimum Qualifications PhD in the field of Planetary Science
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School of Natural Resources and the Environment at the University of Arizona seeks a Postdoctoral researcher to contribute to our project to understand the plant, microbial, and abiotic processes
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, written, and graphic formats. Proficiency in using computers to automate tasks. Minimum Qualifications PhD in Biomedical Sciences or a related field of study. Preferred Qualifications • Microscopy
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PhD in Physics, or related areas (with a focus on quantum optics/AMO physics/QED). Preferred Qualifications Demonstrated experience with cavity QED, many-body atomic physics, atom arrays, driven
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activities as assigned. Minimum Qualifications The minimum qualification for this position is a PhD. The selected candidate must have a conferred PhD upon hire. Preferred Qualifications FLSA Exempt Full Time
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murine BMT models, and donor graft manipulation and pharmacological interventions. Candidates should have a PhD or MD and a strong foundation in cellular and/or tumor immunology. Candidates with a
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. Contribute to grant writing. Minimum Qualifications PhD in computer science, computational biology, or related field. Dissertation must have focused on development of neural networks for analysis of proteins