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processes. The PhD candidate will combine materials science, computational modelling to design a novel framework that identifies optimal materials and AM processes based on performance, sustainability, and
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spaces for applications. To optimize tACS towards a technique of network stimulation in the human brain, we use computational modeling at the population level (neural mass models) as well as at the neuron
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production of high-value chemicals, such as fuels and organic molecules, from “waste” substrates, for example, carbon dioxide and biomass. The main aim is to understand, develop, and optimize electrochemical
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mathematics. Strong background in mathematical systems and control theory. Experience and/or a keen interest in the field of hybrid dynamical systems, observer design, learning techniques, and optimization
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OCT system must be optimized in terms of noise and detection efficiency. You will do this based on experiments and physical modeling of SC, detector, and system performance. Second, the OCT system must
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-of-the-art AI solutions (machine learning, reinforcement learning, optimal control, neuromorphic computing) that help bring the consortium forward in modelling and understanding biological intelligence