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Are you passionate about advancing sustainable mobility solutions? Do you enjoy working at the intersection of artificial intelligence, optimization, and energy management? We invite applications
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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central role in streamlining and standardizing the design flow for quantum device fabrication. This includes implementing and improving design rule checks (DRC), optimizing and debugging code, and
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optimize catalysts for the production of sustainable aviation fuels from bio-based feedstocks. The use of bio-based feedstocks results in new challenges and the optimal catalysts as well as the relevant
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generalized, cross-layer defense framework that integrates network-level mitigation and application-level optimization to comprehensively protect distributed AI training from network threats while maintaining
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the theory of optimization algorithms and high-dimensional statistics to address some of the most fundamental questions in ML such as the behavior of neural networks. The environment of this project is highly
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on designing and synthesizing advanced materials for next-generation batteries—such as solid-state, multivalent-ion, or aqueous rechargeable systems—while collaborating with the research team to optimize
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on interpretable, learning-based stochastic optimal control for over-actuated electric vehicles—vehicles with more actuators than degrees of freedom, which enable sophisticated control strategies but also increase
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-neutral fuels, there is a possibility to achieve fossil free shipping. Hard wing sails are a common wind propulsor, but optimizing their performance requires advanced sheeting strategies to prevent stall, a
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academia as well as industry/the public sector. In this Postdoc project, the particular focus is to evaluate technologies for pelagic fish protein up-concentration. Optimizations will be done against e.g