39 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"University-of-Exeter" positions at University of Twente
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Vacancies Postdoc position on Federated/Continual Learning for Time-Series IoT Data (TRUMAN Project) Key takeaways In this role, you will address the intricate challenge of enabling AI to learn
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Exploiting the Geometric Landscape of Infinite-Dimensional Sparse Optimisation” led by Dr. Marcello Carioni at the University of Twente. More information about this here . Infinite-dimensional modelling has
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in three main areas: Systems Security, Data Security, and AI Security (including both the security of AI systems and AI-driven security techniques). We are looking for a dynamic and ambitious
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Information and application Are you interested in this position? Then apply before February 4, 2026, via the 'Apply' button below and attach your CV and motivation letter. For more information about this
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to what extent data poisoning attacks can influence the output of LLM models in security and safety critical infrastructure. 3. Perform the attack under different scenarios and model the impact. 4. Evaluate
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, 2020 . In this PhD project you will work on applying RNPU networks for solving computational problems that are considered hard. Information and application Are you interested in this position? Please
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, fundamental research and/or studies involving matters of scientific urgency. Information and application Are you interested in this position? Please send your application via the 'Apply now' button below before
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approach that combines semantic material data, design-for-circularity, and hub logistics to scale high-quality reuse in regional infrastructure ecosystems (primary focus: Twente; validation: Brabant). Reuse
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. Designing predictive control strategies that regulate muscle-tendon loading via wearable exoskeletons. Implementing and testing control algorithms in simulation and real-time settings. Information and
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will