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, privacy-enhancing technologies, and trustworthy AI: stating requirements precisely, designing methods that meet them, and building prototypes fast enough to use. Applicants should hold, or expect to obtain
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with predictable and repeatable operations. Autonomous systems need to be predictable and verifiable. Regulatory acceptance requires demonstrable evidence of safety performance. The complex design of
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design. The programme aims to promote social science with the city. Whether working with activists or policymakers, medical doctors or social workers, the goal is to learn from local partners and work
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 24 days ago
Job Code: MPIDS-W093 Job Offer from August 25, 2026 In the Max Planck Research Group “Theory of Turbulent Convection” of the department “Fluid Physics, Pattern Formation and Biocomplexity” we seek
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prediction to systems that reason, plan, interact and act in the physical world. This PhD addresses efficient long-horizon task execution in Physical AI—complex tasks needing sequences of decisions, subgoals
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develop knowledge that supports the hydropower industry in the safe design of future dams and management of existing embankment dams, including those subjected to flexible hydropower operation. Emphasis
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utilize low-grade waste heat. The project aims to develop methods for the design, optimization, and operation of integrated systems that maximize both economic performance and environmental benefits
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, research and innovation; collaborative people who love creating and spreading knowledge; brilliant people who seek growth and want to make a positive difference. At LSBU Group, you’ll be trusted and
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a key challenge in sustainable process engineering: translating microbial electrolysis cell (MEC) technology from lab-scale innovation to robust, industrial deployment. The research will sit at
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-Informed Neural Networks (PINNs) and related approaches for network-wide traffic prediction Design physically consistent and interpretable machine-learning methods for dynamic traffic systems Test and