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unintentionally bring about macro-level outcomes. The group at IAS focuses on the dynamic processes that lead to a concentration of individuals with certain socio-economic or ethnic characteristics in different
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work will increasingly focus on wet lab experiments using cancer cell lines, organoids, and animal models, including imaging and molecular analysis. The project is well-suited for candidates with a
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radar signals), (ii) Medical image analysis, and (iii) Machine learning/artificial intelligence. The group boasts extensive experience in fundamental research within computer vision, machine learning and
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Spatial metaTranscriptomics methods and thus also handling of image data. The PhD student will interact with other team members to a large extent. For this purpose, we are looking for a PhD student with
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is now seeking a professor in pharmacology with a preclinical focus. The department has access to a wide range of methods, including advanced fluorescence microscopy (from single-molecule imaging
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) strategies, primarily revolving around interpretable ML and generative AI, to study complex biological processes. This project combines timely analytical challenges with deep rooted applications in life
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fluorescence microscopy (from single-molecule imaging to intravital microscopy), electrophysiology, respirometry, microfluidics, organoid cultures, bioprinting, and excellent opportunities to work with various
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are particularly interested in the neuropsychiatric symptom picture in Parkinson's disease and in developing better treatment. We have a specific interest in the anatomical-functional association within the STN, and
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growth by metalorganic vapor phase epitaxy and developing AI approaches for deterministic synthesis to achieve n- and p-type conductive AlN and related UWBG Al containing alloys. Doping and processing
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include Drive research projects which include analysis of tissue images from multiplex immunofluorescence, spatial proteomics and transcriptomics Drive development of deep learning and computer vision tools