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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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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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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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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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, design and characterization of quantum processors Development and optimization of nano-fabrication processes for large-scale devices Development of optimal control techniches to achieve fast and high
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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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urban catchments. Develop road designs that optimize water accumulation or flow. Create a framework for the selection and design of climate-adapted roads. The research will primarily involve hydrological
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large