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Field
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
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Doctoral Associate Duke Department of Neurosurgery This Postdoctoral Associate position will play a critical role in making the use of new imaging tools more automatic and streamlined. Key responsibilities
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multimodal brain imaging techniques with novel neuromodulation. More specifically, we work to understand the mechanisms of (mal)adaptive plasticity and develop new treatment approaches for different
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Assistant (Doctoral Candidate) with specialising in hyperspectral imaging (salary scale 13 TV-L, 75 %) The fixed-term position is for a duration of 36 month in accordance with the project duration. Your role
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, catalysis studies - in particular electrocatalysis and Python programming Experience within the following fields is highly beneficial: Transmission electron microscopy, Electrochemistry, Image analysis, Big
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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functional cell assays Interest in translational leukemia research and innovative imaging and omics technologies Bioinformatics experience, especially in R Ideally initial experience with image analysis, laser
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mechanisms underlying psychosis. The research utilizes advanced multiphoton imaging and virtual reality platform for simultaneous recording of animal behaviour and microcircuit activity. RESPONSIBILITIES
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Alex Leung (Mechanical Engineering at UCL) will also collaborate. he specialises in imaging of additive manufacturing and will support the project by assisting with the in-process monitoring. We expect
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into goal-directed behavior. We use state-of-the-art approaches including functional brain imaging, automated behavioral analysis, and computational neuroanatomy. We value a collaborative atmosphere, early