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imaging and biosensor techniques, across digital health and biological modelling, to biopharma technologies. The department has a scientific staff of about 210 persons, 130 PhD students and a technical
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learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing and implementing NLP pipelines for clinical text processing, semantic annotation
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the clinic and in silico. We focus on neurodegenerative processes and are especially interested in Alzheimer's and Parkinson's disease and their contributing factors. The LCSB recruits talented scientists from
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machine learning methods are a plus. Qualifications: PhD in neuroscience, or related fields DeepLabCut or similar methods Demonstrated hands-on experience with 2-photon imaging techniques Experience
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studies fundamental processes in endothelial cells driving cardiometabolic diseases including atherosclerosis, thrombosis and type 2 diabetes. In particular, the team interrogates the role of endothelial
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into the regulation and function of diverse cellular processes. To this end, we use Vaccinia virus as a model together with quantitative imaging and biochemical approaches to study a variety of cellular processes
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to interact and collaborate to develop robust ways to decode single molecule imaging data. Your profile The candidate should hold a PhD in biophysics, chemistry, nanoscience or related subjects and have a
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are looking for a highly motivated and dynamic PhD student for a position in the Translational Neuroscience team headed by Prof. Rejko KRÜGER. The Translational Neuroscience team's focus lies in
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be used to prepare lamella samples for high resolution cryo-EM imaging and tomography. From AI assisted image analysis, 3D models for key proteins and biomolecular complexes will be fitted into 3D
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heart failure Using advanced imaging and experimental medicine to understand disease mechanisms and predict therapeutic success Unravelling the impact of ageing on heart failure, particularly in HFpEF