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); an affinity for one or more of: world models, generative modelling, causal/representation learning, reinforcement or imitation learning, dynamical systems, or robotics; the ability to formulate and pursue
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-Facilitated Blended Interaction Learning Spaces: Project framework A persistent challenge in higher education is the asymmetry between learning formats: lecture-based teaching offers students clear pathways
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(e.g. Agentic Reinforcement Learning), evaluation, tool use, agentic harness, or retrieval-augmented systems. Internship/full-time experience from research, engineering, or algorithm-development roles in
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are excited about future wireless and satellite communication systems, we encourage you to apply—even if you do not meet every item in the project description. We value candidates who are eager to learn
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components. You will explore how learning-based methods, such as imitation learning and reinforcement learning, can be integrated with model-based low-level controllers and multimodal sensing to enable contact
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, sensors and instrumentation. Knowledge of one or more of the following areas:Power electronics, Reliability and failure mechanisms, Sensors and instrumentation, Data analytics, machine learning, or AI
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work environment. To reinforce that, we have committed ourselves to the joint Statement Social Safety of the Association of Universities in the Netherlands and the National Action Plan for Diversity and
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hiring a doctoral candidate on the subject "Foundation AI models for distribution systems decision-making ". Foundation models have recently emerged as a new learning paradigm in AI. These models learn
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mechanisms, Sensors and instrumentation, Data analytics, machine learning, or AI, Digital twins or physics-based modeling, wide-bandgap semiconductor technologies. Excellent written and spoken English
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emerged as a new learning paradigm in AI. These models learn from large datasets through self-supervision and have proved to generalize across many applications. Successful examples of foundation models