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of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference code: 980 - 2026/WD 1
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analytics Exploring and implementing federated learning and privacy-preserving AI approaches for distributed clinical datasets Collaborating closely with data providers, clinicians, and technical teams
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Collaborative Research Centre 1748 ‘Principles of Reproduction’. The CRC 1748 involves scientists of the University of Muenster, the University Hospital, and the Max Planck Institute Münster as well as scientists
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with experimental and clinical collaborators. With the help of statistics, machine learning and pathway- and network- and analyses, the goal is to improve the mechanistic understanding of disease- and treatment
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Description We are seeking a motivated new PhD candidate who wants to join an exciting collaborative research program within the VIB-Center for Inflammation Research between the Guilliams and
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detection, and uncertainty assessment Design and evaluate robust data pipelines for industrial monitoring systems Participate in project meetings, workshops, and collaboration activities within the GreenFab
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: Advanced deep learning architectures Mathematical foundations of machine
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methods Excellent programming skills and familiarity with modern deep learning frameworks Strong interest in interdisciplinary research, and the ability to engage meaningfully with collaborators from
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Senior Researcher in Ecosystem based marine management, nature restoration, and anthropogenic imp...
impact Innovativeness, including commercialization and collaboration with industry Leadership, collaboration, and interdisciplinary skills Communication skills Salary and terms of employment
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About us VIB.AI, the VIB Center for AI & Computational Biology, is a research center dedicated to integrating machine learning with deep biological insight to understand complex biological systems