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Field
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studies of orbital currents and conversion mechanisms to optimize the performance of spin-orbit devices Moirés of orthorhombic van der Waals magnets Exploring and controlling curvature-induced non
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application! We are looking for a PhD student in automatic control. Your work assignments You will work within a project focused on optimization, planning, and decision-making in control systems involving
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computing architectures, replacing conventional structureless and deterministic LIF point-neuron models. This is pursued through circuit designs that exploit and control memristor dynamics (e.g., local
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such as autonomous systems, complex networks, data-driven modeling, learning control, optimization, and sensor fusion. The division has extensive collaborations both with industry and other research groups
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control theory Knowledge of machine learning or optimization methods is a plus Proficiency in programming (Python, Matlab, C++, or similar) Strong interest in cutting-edge research on 6G and autonomous
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green transition. Due to the complexity and heterogeneity of the biological mechanisms underlying fermentation, achieving optimal performance often requires fine-grained, time-dependent, control
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well as postgraduate and undergraduate education within areas such as autonomous systems, complex networks, data-driven modeling, learning control, optimization, and sensor fusion. The division has extensive
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solutions that maximize operational efficiency, minimize network impacts, and ensure the long-term sustainability of the proposed approaches. The project investigates optimized EV charging strategies aligned
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Description The PhD researcher will work on the Inventory-Routing Problem with Pickups and Deliveries (IRP-PD) under demand and supply uncertainty. The IRP-PD requires the joint optimization of inventory and
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such as surgery, patient or staff scheduling using, e.g., multi-objective optimization or machine learning approaches and analyzing efficiency-fairness trade offs. The research will be conducted under