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language processing, and more. We own and operate the entire technology stack for Machine Learning Operations. This ensures that the models we build translate into secure, reliable, and actionable outcomes across
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Deep Learning (CIDL), part of the Leiden Institute of Advanced Computer Science (LIACS). As a team, we develop cutting-edge techniques for advanced computational imaging systems, combining expertise from
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Vacancies 2x PhD positions in the Mathematical Foundations of Machine Learning on Graphs and Networks Key takeaways The Discrete Mathematics and Mathematical Programming (DMMP) group
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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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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you passionate about advancing Machine Learning by integrating
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approaches combining control, learning, and uncertainty quantification. This project develops a data-driven control framework grounded in first-principles models with emphasis on: Data-driven practical
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students who would like to teach our first-years students and help them to integrate in the university. Do you want to make the difference for our students? All first-year students will be enrolled in
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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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contribute to the development of innovative, physiology/ machine learning-driven clinical solutions and decision support tools for critically ill patients, focusing on cardiovascular and respiratory monitoring
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-physical systems secure and resilient in the presence of uncertainty and cyber-physical attacks? Then you may be our next PhD candidate in resilient and learning-based control of cyber-physical systems