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garments into recycle, reuse, and manual-review streams; this PhD project tackles the core challenge of designing and optimizing a high-throughput hyperspectral imaging system, fused with complementary
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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to research this topic. Interest in laboratory work and basic technical understanding. Fluent written and spoken English. Programming skills in e.g. Python, R, Matlab and Java. Experience in image processing
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well as to initiate new queries. As a member of an interdisciplinary team, the candidate will have the opportunity to receive training in a variety of techniques, including molecular biology, embryology, imaging
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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
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assaulting them. Second, technology has contributed to the creation of new forms of sexual harm, such as image-based abuse and deepfake technology. The implications of these developments are twofold. First
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violence, such as when perpetrators take pictures of their victims while assaulting them. Second, technology has contributed to the creation of new forms of sexual harm, such as image-based abuse and
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procedure Icon Applicants Applicants Icon Foundation Foundation Icon Notification Notification Nomination Examinationapprox. 1-2 months Review Processapprox. 3-4 months Conferral Nomination documents
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks