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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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studying how wireless sensing and AI interact in real systems. The work will be carried out in close collaboration with researchers in wireless communications, sensing, machine learning, and robotics, with
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, you will work at the intersection of polymer processing, materials science and Machine Learning to develop dynamic recipes for sustainable plastics. In a typical plastics production line, several types
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, ultimately, predictable by machine learning. Specifically, you will build a first-in-class framework to expedite the design of high-affinity binders that engage with therapeutic targets or efficient (bio
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machine-learning strategies for multimodal representation learning and for the integration of the complex biological datasets generated within the consortium. Duration and start date: 48 months, full-time
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discipline. Essential Good programming skills, preferably in Python/C#. Experience with machine learning, deep learning, or experimental AI evaluation. Interest in secure distributed AI, federated learning
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Join the Responsible Machine Learning (ML) Group at the Faculty of Computer Science. Led by Prof. Dr. Martin Pawelczyk, who recently joined the University of Vienna from Harvard University, our research
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, statistical inference and machine learning, with potential applications to practical problems Independent teaching Supporting research work, participation in research projects, and assistance with teaching and
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automotive and aerospace electrification. Applications for this PhD position are invited at the Power Electronics and Machines Centre, University of Nottingham. Based in a recently built £18M facility
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with a strong background in mathematics, computer science, or machine learning. The work has a strong focus on developing new objectives or new architectures for medical deep learning and on new ways