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of performance, as they are very cautious by design. This, in turn, makes them less practical for problems where speed is of utmost priority. On the other hand, offline learning, such as Deep Learning, often
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optimization methods for run-time network configuration and control. You will design efficient and lightweight learning-based techniques for automated scheduling, network resource allocation, and parameter
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how learning activities, assessment methods, and course design can reinforce each other and contribute to meaningful learning experiences. Part of your work will involve designing and testing new
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develop the ability to address complex real-world challenges. This includes exploring how learning activities, assessment methods, and course design can reinforce each other and contribute to meaningful
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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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23 Mar 2026 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Design engineering Engineering » Systems engineering Environmental science
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into answering counterfactual questions. Using remote sensing multimodal time-series data and Earth foundation model embeddings, you will design and develop causal machine learning models tailored for dynamic
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explainable AI (XAI) methods with user-centred interaction design, combine machine learning with alternative AI methodologies (e.g., rule-based reasoning, knowledge graphs, hybrid approaches where relevant
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for both youth and police. You will collaborate with stakeholders, end-users, game designers and our CONTEXT science team to improve our virtual reality game and its biofeedback algorithms. You will perform
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this project, you will work with the team at M4i, Maastricht University to design, develop, and test the novel IR-MALDI-MSI source. Then, in the next phase, you will install and operate this source at HFML-FELIX