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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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applied AI/HCI or distributed systems and architectures, including one or more of the following: optimisation, machine learning/XAI, interactive visualisation, offline-first or mobile systems, secure data
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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
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, database theory, and edge and cloud computing, this research will pave the way for a new reasoning paradigm that unites the expressiveness of materialisation with the efficiency of virtualisation. You
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and turned into insight). To tackle this, the research blends ideas from knowledge graphs, stream processing, database theory, logic, edge and cloud computing, and Artificial Intelligence into a