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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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(m/f/d) in the topic: “AI-based processing of CAD models for automated planning of computer-aided manufacturing.” The candidate has the opportunity to pursue a doctoral degree (Ph.D.). Remuneration is
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. The group’s work in this area has led to best paper awards at PacificVis and Graph Drawing, with recent publications in IEEE Transactions on Visualization and Computer Graphics and Computer Graphics Forum
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University of Veterinary Medicine Hannover • | Hannover, Niedersachsen | Germany | about 18 hours ago
. Their curriculum vitae should reflect their particular skills and motivation. Language requirements Applicants must provide proof of their English skills. English: TOEFL 550 (paper-based), 213 (computer-based), 79
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University of Veterinary Medicine Hannover • | Hannover, Niedersachsen | Germany | about 18 hours ago
of their English skills: TOEFL 550 (paper-based), 213 (computer-based), 79 (internet-based) or equivalent Application deadline Applications may be submitted at any time. Submit application to PhD@tiho-hannover.de
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/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 packages
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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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maintains strong ties with the local experimental groups of TUD and the institutes of the DRESDEN-concept environment. The chair hosts its own computer cluster and has full access to the high-performance
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literacy and basic programming skills (R, Python), or the willingness to develop these skills Analytical mindset, with a keen interest in advancing proteomic methodologies We offer Cutting-edge proteomic
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta