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to science, engineering and society. You will focus on optimization-based, data-driven and partially model-based control methods. You will have access to a strong research network and a broad range of
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dissipative networks. We will view learning as a feedback interconnection of continuous-time (circuit) dynamics and an optimization algorithm. The key idea is to develop algorithms that minimise cost functions
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advanced algorithms for solving complex optimization problems. You will work with real-world case studies to design future-proof supply chain networks for key agricultural products in vulnerable regions
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optimization of these building blocks are essential for achieving breakthroughs in data rate, power efficiency, scalability, and overall system cost. This PhD project focuses on the electronic integrated circuit
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selection and validation, supervised and unsupervised learning, optimization techniques, (deep) neural networks, probabilistic methods and statistics, data visualization, natural language processing
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confidence in the measurements and establish traceable validation routes. Optimize the methods for realistic converter operating conditions and communicate practical guidance to academic and industrial users
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suitable wireless link at any moment. In this PhD project, you will develop novel concepts for intelligent hybrid RF–OWC networks that optimize wireless service delivery in real time. Research topics include
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-centric AI and explainable decision-making? Are you intrigued by the role of human expertise in complex optimization? This PhD position focuses on bringing humans into the loop of optimization processes
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catalyst structure–performance relationships. The most promising catalysts will then be optimized and evaluated under practically relevant electrolysis conditions, contributing to the development of a
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incentives, network constraints, and regulatory decisions in shaping collective outcomes. Identifying optimal designs and interventions is further complicated by multiple, often competing, objectives