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Post-Doc in Computational Genomics and High-throughput Genome Editing Leuven VIB-KU Leuven Center for Microbiology Post-doctoral Posted Date 13 Oct 2026 Locations Leuven Center VIB-KU Leuven Center
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learning. Experience in computer vision, digital pathology, whole-slide image analysis, self-supervised learning, foundation models, multiple-instance learning or high-performance computing is an
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protein structure and protein interaction networks; AI-based modeling of cell morphology and tissue function using imaging and computer vision; AI models of disease and digital twin applications. We
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering Leuven VIB Center for AI & Computational Biology Group Leader / PI Posted Date 12 Oct 2026
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using imaging and computer vision; AI models of disease and digital twin applications. Biological applications and disease areas ideally focus on genetics and genomics. We also welcome applicants with
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
protein interaction networks; AI-based modeling of cell morphology and tissue function using imaging and computer vision; AI models of disease and digital twin applications. We also welcome applicants
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
protein interaction networks; AI-based modeling of cell morphology and tissue function using imaging and computer vision; AI models of disease and digital twin applications. We also welcome applicants
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of protein structure and protein interaction networks; AI-based modeling of cell morphology and tissue function using imaging and computer vision; AI models of disease and digital twin applications
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, enthusiastic, dynamic, mobile and communicative researcher with a Master of Science degree in Engineering (preferably mechanical, energy, chemical or computer science). The candidate should have a
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, Computer Science, or a related field. You have experience with power, thermal, or energy-efficiency modelling. You have a solid understanding of computer architecture and heterogeneous computing