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Field
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that exhibit emergent turbulent behaviors, and (2) disordered optical media that process information through complex light scattering patterns. Using advanced imaging, machine learning techniques, and real-time
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The main task is the further development of methods for the non-destructive investigation of the pyramids of Giza in Egypt (World Cultural Heritage) in cooperation with our partners by using acoustic imaging
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for immunological platforms such as flow cytometry, single-cell transcriptomics, spatial transcriptomics, metabolomics and proteomics, imaging, and in vivo work. Numerous interactions with other groups working in
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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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cyanobacteria and Paramecium bursaria. Image data analysis using AI-based tools and programming analysis scripts. Participation in conferences, presentations, and preparation of publications. Intensive exchange
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-performance computing and imaging facilities. a collegial, international atmosphere and flexible, family-friendly working hours a structured PhD programme with extensive training and career-development
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domains are e.g., signal-/image processing, artificial intelligence and machine learning. Tasks: research and development in designing and programming field programmable gate arrays (FPGAs) for accelerating
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Imaging of mouse bone marrow Analyses of clinical data and correlation with wet lab findings Your profile Master degree (or similar) in molecular biology, biomedicine, biotechnology, bioinformatics or a
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Course location Marburg Description/content MARA is a university-wide institution spanning all disciplines and faculties that bundles the offers for the promotion of doctoral candidates and postdocs
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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