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, based on measured air temperature and humidity data in cold chains by commercial sensors, and deploy them in end-to-end virtual supply chains. This project also aims to optimize other thermal processes
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approach to (1) better understand and steer refrigeration processes or packaging design by optimizing for product quality loss and waste, and (2) connect the improved quality preservation from digital twins
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an interest in bridging rigorous theoretical insights with challenging real-world tasks. They will also explore reinforcement learning strategies to optimize decision-making policies in complex
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worldwide in industrial and academic research labs for device and material optimization as well as for the study of device physics. Leading manufacturers and academic institutes rely on our tools for electro