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Field
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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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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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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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13 Mar 2026 Job Information Organisation/Company Christian-Albrechts-Universitaet zu Kiel Department Department of Materials Science Research Field Engineering » Materials engineering Researcher
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based on machine learning. Reference number 08/26 Your tasks 1. Assessment and analysis of GaN technology characterization data Identification of outliers during testing, with and without machine learning
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are designing the technologies and processes for the future of digital production, packaging and scanner technology, and many other laser-based applications, such as 3D printing and laser fusion. Are you looking
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. Methods, 2024). The approach is still in its infancy; hence we need to drive the further development of the imaging technology, labeling agents and analysis approaches at the same time. To demonstrate
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connecting AI, computational biology, human–computer interaction, and research software engineering. Close collaboration with the Helmholtz AI Consultant Team, providing direct exposure to a broad range of
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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time
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imaging. Your Profile: The successful applicant must have the following: • Master’s degree in physics, biophysics, biomedical engineering, computer engineering or electrical engineering. • Excellent track