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engineering, or similar. Solid mathematical and analytical skills, including signal processing and optimization. Knowledge about classical and/or quantum data communication, including for instance error
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in computer science or applied mathematics and a strong interest in electrochemistry, molecular modeling, and sustainable energy technologies. Experience with computational methods, data analysis
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algorithmic solution development. The group focuses particularly on automated decision-making in autonomous cyber-physical systems, combining mathematical optimization, machine learning, and decision theory
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assessment. Design and train reinforcement learning agents to optimize operational safety. Build and validate dynamic Bayesian network models integrating empirical and synthetic data. Conduct scenario-based
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alerts, facilitating optimized maintenance decisions and reducing unplanned downtimes. The framework will be validated through close collaboration with industry partners, incorporating actual operational
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developed to allow operators to visualize real-time risk indicators, maintenance sched-ules, and preventive repair alerts, facilitating optimized maintenance decisions and reducing unplanned downtimes