14 computer-science-image-processing Postdoctoral positions at Technical University of Munich
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: PhD in CS/ML/medical informatics, strong publication record, and hands-on experience with generative models in medical imaging. Postdoctoral Research Associate (f/m/d) EU Research Project TWIN-X Full
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of miniaturized imaging technologies. Your Profile: The successful applicant must have the following: • Ph.D. in natural sciences, electrical engineering, physics, optics, medical technology, biomedical computing
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learning, medical image computing, biomedical engineering, medical physics, or related field Strong Python and PyTorch experience Solid publication record and ability to communicate research results
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invasive in blood biomarker detection. In collaboration with our partners at the Karlsruhe Institute of Technology (KIT), you will develop data analysis pipelines as well as signal processing and inference
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Informatics Initiative (MII)/FHIR standards Design and implement methodological concepts and software for benchmarking frameworks for AI evaluation Independently develop and implement research ideas within
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science, and computational imaging. • Access to state-of-the-art HSI and spectroscopic instrumentation (HySpex VNIR/SWIR systems, Macro-XRF, Raman, FTIR, etc.). • Active participation in international in
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programming for workflow automation and data handling Familiarity with high-content imaging and quantitative analysis is a plus Interest in interdisciplinary approaches at the interface of biology, technology
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efficiency while keeping the grid reliable and secure. Our research method is engineering-oriented, prototype-driven, and highly interdisciplinary. Our typical research process includes the evaluation
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, interns, and PostDocs at the intersection of computer vision and machine learning. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100
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, (Bio-)Informatics, Computer Science or related disciplines Strong background in modeling multi-modal data (images, tables, text, etc) Understanding of biases and causal inference Experience with machine