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- Forschungszentrum Jülich
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vision-based platforms. You will work at Forschungszentrum Jülich, in close collaboration with bioimage analysis partners at Karlsruhe Institute of Technology. Your tasks in detail: Develop and extend deep
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
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Description The Research Training GroupGRK 2868 D³ - Data-driven Design of Resilient Metamaterials funded by the German Research Foundation has started in October 2023. Our vision is to develop and
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information about our institute here: https://www.fz-juelich.de/en/ias/ias-8 Your Job: Develop 3D+t image reconstruction methods in a cell microscopy setting using image sequences as well as focus stacks
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statement that outlines your vision for contributing to one or more of the focus areas) by April 6, 2026 (stamped arrival date of the university central mail service or the time stamp on the email server
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your chance! We are looking for a highly motivated candidate who is interested in infectious disease research with focus on helminths and who shares our vision to improve the World’s health. Our mission
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institution. At the Faculty of Computer Science, Institute of Artificial Intelligence, the Chair of Machine Learning for Computer Vision offers two full-time positions as Research Associate / PhD Student (m/f/x
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reproducible research practices. Your responsibilities Develop and implement computer vision and image processing algorithms for star tracking and satellite detec-tion using event cameras. Design and build a
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comprises the day-to-day leadership of the GIGA in collegial collaboration with the GIGA team and in line with its institutional vision. The professorship entails research and teaching in the field
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, data science, applied mathematics, physics, materials science, or a related field. Solid background in machine learning and/or computer vision. Interest in representation learning, active learning