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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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of on-site construction production processes, as well as sustainable development. You will also demonstrate independence, creativity, and strong collaboration and analytical skills. Research environment
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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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of Electrical Engineering . You will be supervised by senior researchers with expertise in robotics, machine learning, automatic control, and optimization. The group leads and participates in numerous
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We are looking for a highly motivated, skilled, and persistent PhD student with experience in computational fluid dynamics (CFD) and some knowledge in structural analysis. The research aims
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activities are focused on the modelling of the mechanics of structures, components, and processes along with involved materials, thereby providing mechanics-based solutions and virtual solution tools to our
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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 building the next generation of quantum processors based on superconducting circuits. To achieve this ambitiuous goal, we have a variety of projects related to: Development and optimization of nano
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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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behavioural, organizational, and ergonomic factors—such as discomfort—may contribute to traffic-related risks. Through close collaboration with stakeholders in the healthcare, construction, and technology