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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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that are both fast and adaptive? This thesis aims to develop a robust hybrid learning framework that lies at the nexus of online and offline learning. The developed algorithms should be able to benefit from
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required. Are you passionate about mathematical systems and control theory? Do you want to develop cutting-edge control algorithms for the security and resilience of cyber-physical systems? We welcome you to
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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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this postdoc position, the focus is on methods and algorithms for large-scale graph analytics, in particular network science approaches for analyzing longitudinal, population-scale relational data derived from
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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
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given model. As a second task, you will work on software development for model learning, and in particular, on the Python library AALpy . Model learning is done algorithmically, by sending inputs to and
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our biofeedback algorithms and, after small N assessments, contribute towards setting up a large-scale trial to assess the efficacy of the newly developed methods. For this you will interact extensively
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(LES) results. Key Responsibilities: Develop and refine numerical algorithms for real-time wind field forecasting. Validate forecasting models against high-fidelity LES data and field measurements
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for their governing mechanisms? How can we make these model computationally efficient and capable of scaling to large dataset sizes? Scientific challenges for this exciting PhD project include: develop principled