40 image-processing-"Embry-Riddle-Aeronautical-University" Fellowship positions at University of Michigan
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Apply Now Job Summary The Transplant Surgery Research Fellow, supported by the University of Michigan Section of Transplantation and the UM Health Transplant Center, is a new position managed in
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* Experience in one or more of: AAV/vector biology, in vivo gene delivery, mouse models/surgery, single-cell/spatial transcriptomics, ATAC-seq, microscopy, quantitative imaging, computational analysis (R/Python
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environment in which people treat each other with respect and dignity, regardless of roles, responsibilities or differences. Providing support, direction and resources enabling us to accomplish the
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plasticity. We study questions at a variety of levels, Ranging from synaptic and cellular studies using patch-clamp electrophysiology, large-scale population recordings using 2-photon Ca2+ imaging in awake
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. Experience working with medical images. Very good computer programming skills and physics background is essential. Desired Qualifications* Nuclear medicine imaging/dosimetry experience. Experience in image
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, operation of the scanner, data acquisition and analysis, image evaluation, and statistical analysis. The fellow will also be expected to prepare manuscripts and conference abstracts related to projects and
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, neuroimaging, and have relevant experience. The applicant should be proficient with Sleep Profiler EEG acquisition, PET and MRI imaging processing, and statistical analysis tools. Fluency in English and teamwork
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experimental skills related to high-speed imaging, X-ray imaging, data acquistion, and signal filtering are also appreciated Interest in the translation of fundamental physics to real-world applications Modes
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communication skills; commitment to a team-oriented environment. Desired Qualifications* Experience with single-cell genomics, advanced imaging, or bioinformatics. Proficiency in R / RStudio, Python, or similar
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include, but not limited to, computational approaches such as AI and machine learning; methodological foundations and computational approaches for AI for biomedicine, Bayesian inference, cancer imaging