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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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elucidating the molecular and cellular mechanisms of the late phase of long-term potentiation (LTP), a key process in learning and memory. The project is based on the development and use of an innovative
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of electron microscopy and beam-material interactions. The ideal candidate will have a basic understanding of image processing. Core competencies: – Knowledge of image processing software (Avizo, Dragonfly, etc
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imaging hardware components Preferred License: No If yes, what is the preferred licensure/certification?: Preferred Computer Applications: Not Applicable Preferred Other Computer Applications: Preferred
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). Prepare biological specimens for optical imaging workflows, including fixation, embedding, sectioning, labeling and mounting. Optimize staining protocols and tissue/cell processing methods to ensure
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are seeking a Ph.D. student to join our multidisciplinary team developing a radical solution for better detection and treatment that uses ultra-thin snake-like robots and advanced optical imaging techniques
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optimal imaging options for most common MRI exams to minimize artifacts and decrease scan time. Post process if needed. Calculate and administer appropriate contrast for exam. Administer contrast under
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enterprise and are within the top 1% of universities worldwide. Apply to be part of a Horizon Europe Pathfinder science-to-technology breakthrough, targeting a paradigm shift in fabricating a novel optically
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visit: https://policy.ucop.edu/doc/1001004/Anti-Discrimination Because we want you to feel seen and valued, our recruitment process at UC Davis supports openness and authenticity. Research shows that some
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electrophysiology, imaging, and behavioral methods, with rodents as the model system. A major challenge that makes learning difficult is that the world is complex and high dimensional: there are often multiple