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the liveable cities of tomorrow? Job description Human-centred AI techniques, such as Reinforcement Learning from Human Feedback (RLHF), hold great potential for supporting design methodologies in urban planning
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team at AMOLF, working on fundamental questions on physical self-learning systems as part of the NWO ENW‑M1 project “How do physical learning systems learn?”. The research position is intended to start
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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and
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that strengthens collaboration, reduces time-to-job, and drives innovation for a climate-neutral society. In this position, you will design and optimize learning communities that integrate learning
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models that provide evidence-based reasoning for mission-critical decisions. Explainable AI for mission-critical decision support: design interpretable machine learning architectures capable of offering
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. Design and evaluate adaptive training programs that integrate cybersecurity, digital literacy, and experiential learning. Innovate methods for assessing learning performance and collaboration in virtual
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part of human dignity, autonomy, and well-being. Yet, for persons with disabilities, sexual rights remain one of the most neglected domains in law, policy, care, and technological innovation. Despite
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, the fusion community has started to develop fast surrogate models based on Machine Learning / AI models to speed up significantly the employed tools. Such tools have demonstrated to be generally applicable and
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University as a postdoctoral researcher driving ScaleUA, an international Horizon Europe initiative strengthening Ukraine’s tech infrastructure and shaping innovation, strategy and governance across Europe
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drives innovation for a climate-neutral society. You will investigate how learning communities can be interconnected across micro, meso, and macro levels to create a sustainable knowledge ecosystem