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, book chapters, and shorter contributions such as blogposts. Some of these will be co-authored pieces with the principal investigator (Prof Van Calster), the postdoc and other team members. Contribution
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articles, book chapters, and shorter contributions such as blogposts. Some of these will be co-authored pieces with the principal investigator (Prof Van Calster), the postdoc and other team members
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, and shorter contributions such as blogposts. Some of these will be co-authored pieces with the principal investigator (Prof Van Calster), the postdoc and other team members. Contribution to the
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shorter contributions such as blogposts. Some of these will be co-authored pieces with the principal investigator (Prof Van Calster), the postdoc and other team members. Contribution to the outreach of the
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postdoc and other team members. Contribution to the outreach of the project, including podcasts and blogs. Public speaking and public engagement, including in international academic networks, with the
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investigator (Prof Van Calster), the postdoc and other team members. Contribution to the outreach of the project, including podcasts and blogs. Public speaking and public engagement, including in
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, scientific computing (in C/C++) and general coding (python, Java) are important parts of this PhD track. The candidate starts from insights in the system and physics-based modelling. Data-driven approaches
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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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version control (Git) and DevOps tools (Docker, Kubernetes, CI/CD pipelines). Familiarity with high-performance computing (GPU, CUDA, Slurm, etc.). Notions of full-stack software development or a
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controlled lab settings (drone cage, motion capture, hardware-in-the-loop simulation, onboard computing) and in outdoor flight campaigns, with application domains including long-range infrastructure