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dedicated to digital transformation in healthcare, sports, food, and environmental monitoring through advanced (bio)chemical sensing, combining electrochemistry and imaging technologies. Led by Prof. María
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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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immunology is highly advantageous. Proficiency in core immunological techniques such as multicolor flow cytometry, ELISA, tissue sectioning and imaging, or in vivo mouse models is strongly preferred
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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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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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, 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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molecular biology, quantitative imaging and biophysical approaches to investigate cell shape changes in cultured cells and in vivo. Current projects in the lab include investigating the regulation
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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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prototype system based on advanced commercial sensors for fall risk assessment and rehabilitation. The project includes both experimental sensor data acquisition and algorithm development. You will be
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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