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into concrete solutions for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and
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for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our
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PhD Studentship: Designing Human-AI Teams for Meaningful Human Control of 'Machine-Speed' Operations
and data capacity of Artificial Intelligence systems, i.e., ‘machine-speed’ operations, without disrupting or compromising such operations? How can we ensure humans retain authority for actions
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 9 days ago
Researcher (R1) Application Deadline 30 Nov 2026 - 15:14 (UTC) Country Germany Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU
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Are you inspired by developing the next generation of intelligent photonic chips? Join us to pioneer heterogeneously integrated photonic technologies for ultra-fast, energy-efficient optical
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player who contributes positively to collaboration and project success. You also possess: a PhD in Artificial Intelligence, Machine Learning, Computer Science or a related field; at least 3 years of hands
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the brain and artificial neural networks, you will contribute to a better understanding of the computational principles underlying auditory intelligence in humans and machines. What you do As a PhD candidate
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Join a cutting-edge research programme at TU Delft and investigate the mechanisms of hydrogen-induced degradation in circular steels. Job description Within this project, you will investigate one
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researcher. The Veldhorst group is a pioneer in quantum computing and simulation in germanium. They have demonstrated the first single-hole qubit, enabled two qubit logic in germanium, executed quantum
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of compact robot models. The challenge is building capable robot intelligence with limited real robot data and compute. Interaction data is costly and slow to collect, while deployment requires real-time