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applications for machine learning. Technology Assessment: Evaluate emerging technologies, instrumentation devices and other tools related to power electronics for electric vehicles applications. Collaboration
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histopathology and clinical information to learn robust cross-modal representations for diagnostic prediction. It pursues two integrated objectives: (i) to develop generative and explainable AI approaches
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; deliver high-quality masters to partners; and document fabrication processes for future implementation. The fabricated samples will be characterised in collaboration with DC1 and DC2 to assess
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decision-making. Working with real-world data from Alliander, you will publish at leading machine learning venues while building tools with tangible impact on the Dutch energy sector. The Dutch energy
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or explainable). Professional Experience: Analysis of scientific literature. Contributions to open source projects, machine learning competitions or relevant own projects. Have participated/collaborated in
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engineering. Together, we learn by making, creating, and innovating, addressing challenges in a solution-oriented way. Quality, connection and inclusivity are the foundation of our culture. In our open
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) for education and professional practice, both at the individual level and/or in collaborative contexts. The project should explore how AI and other emerging digital technologies shape and transform work tasks
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changing work. Its projects examine what happens when generative and agentic AI enters leadership, everyday workflows, team communication, professional learning and career decisions. Across the network
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November 2026 at 23:59 CET Expected start: 1 January 2027, or upon agreement About REGULAIRE The scholarship is part of REGULAIRE (Regulatory Learning for the Governance of Transformative Technologies), a
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Business School Application deadline: 15 November 2026 at 23:59 CET Expected start: 1 January 2027, or upon agreement About REGULAIRE The scholarship is part of REGULAIRE (Regulatory Learning