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probabilistic generative models for networks; analyze real network data from different application domains; design efficient algorithmic implementations of the theoretical models. You will be supervised by Dr
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setting, enabling fast and predictable adaptation with minimal overhead. A central focus is the co-design of algorithms with edge hardware and embedded platforms. You will investigate implementation
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PROTEUS. Ultimately, the causal reasoning and uncertainty algorithms you build will serve as the quantitative engine for the "Copernicus Agent," an AI assistant designed to give European policymakers
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, Engineering, mathematics or related disciplines with a strong background in data analysis, mathematical modeling and algorithms Good programming skills in Python/C/C++ Good oral and written skills in English
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on practical feedback linearization with limited or imperfect models. Learning-enabled control dynamics Embedding optimization and learning algorithms (e.g., SGD, Bayesian updates) into control design and
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-of-information metrics. Propose computational algorithms to estimate these metrics. Design and execute simulation studies to evaluate the above. Develop and test statistical software. Write user-friendly guidance
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strategies (e.g., feature attribution, counterfactual explanations, dialogue-based explanations, hybrid symbolic–ML approaches); develop user-facing explanation interfaces that connect algorithmic reasoning
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May 2026 Apply now Are algorithms neutral tools, or do they actively shape the world they model? In this PhD, you will bridge the gap between building and critically studying Human-Centred AI systems
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Is the Job related to staff position within a Research Infrastructure? No Offer Description Computational geometry is the area within algorithms research dealing with the design and analysis
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a focus. Traditionally, this is done through iterative algorithms (‘trial and error’). In this project, we aim to develop a radically different approach where the correct shape is computed using a 3-D