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Field
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Profile: University degree (M.Sc., diploma or equivalent) in materials science, engineering sciences, or physics Experience in at least one of the methods of X-ray imaging, transmission electron microscopy
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partners regarding the horticultural experi-ments (Osnabrück University of Applied Sciences and the Institute of Plant Genetics of the LUH) and the optical superimposition of imaging with a processing laser
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orbital parameter extraction using image processing techniques. The ideal candidate should have a strong background in physics, engineering or a related field, as well as experience working with programming
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 29 minutes ago
Job Code: 18-25 Job Offer from June 04, 2025 The Department of Prof. Dr. Stefan W. Hell invites applications for a PhD position exploring AI approaches in optical science, molecular imaging and
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the field, from tissue scaffold design in tissue engineering evaluation to surgery. The student will focus on establishing best practice for widespread applicability, building on past skin, heart, lung, brain
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In the Phytophotonics department, you will work as part of an interdisciplinary team at the interface between plant sciences and optical technologies. The focus is on various spectroscopic and imaging
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necessary or desired background A first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. Subject Area Medical imaging, computer science & IT, AI
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the Phytophotonics department, you will work as part of an interdisciplinary team at the interface between plant sciences and optical technologies. The focus is on various spectroscopic and imaging methods
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in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages, PyTorch Familiar with foundation models (vision large models or multi
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(or equivalent) in an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject area: Medical imaging, biomedical engineering, computer science & IT