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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
your research and thereby contribute to the scientific development of the new VIB.AI center. Teaching. The candidate will be appointed at the Faculty of Engineering Science and will take up teaching
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biological questions with direct biomedical relevance. We are embedded in both the VIB-UGent Center for Inflammation Research , a leading center unravelling the cellular and molecular mechanisms
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questions with direct biomedical relevance. We are embedded in both the VIB-UGent Center for Inflammation Research , a leading center unravelling the cellular and molecular mechanisms of inflammatory diseases
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instrumentation for both light and electron microscopy and has been at the forefront of the development of Correlative Light and Electron Microscopy (CLEM) approaches to answer complex biological questions
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guidance, and ensuring a positive user experience. You will: Resolve complex cases that would otherwise block or overload specialist teams. Take over smaller operational tasks from network & infrastructure
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international and interdisciplinary research environment. The successful candidate will work at the forefront of structural biology and drug discovery, addressing fundamental questions with direct relevance
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an experienced drug discovery scientist to lead early-stage discovery programs and help translate VIB's scientific breakthroughs into tangible therapeutic opportunities. The successful candidate will bring strong
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practices and international standards. Leverage VIB Data Core services, including high-performance computing pipelines and large-scale GPU resources, to scale ML development and deployment. Your profile PhD
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. You will split your time between building institute-wide AI tools and applying them directly to neuroscience research challenges. Your role As an LLM Engineer in this shared position, you will: Develop
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of transmembrane β-barrel nanopores. The group provides a highly interdisciplinary environment where computational method development is closely integrated with experimental validation. The successful