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and analysis of dedicated algorithms for training analog circuits directly from data. In this PhD project, you will develop a novel system-theoretic framework for learning in analog circuits and
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‘healthy biodiversity’ into quantifiable impact indicators; Develop a spatial optimization algorithm that finds land use configurations that optimize these indicators; Compute Pareto frontiers under
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the interface between the classical and quantum worlds. You will design QEC codes suited to realistic hardware, develop the decoding algorithms that make them practical, and map the trade-offs between reliability
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of repeatedly solving wave equations. In the project, we will develop a new mathematical and computational framework that combines PDE-based modelling with ideas from data-driven reduced-order modelling. The aim
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worlds. You will design QEC codes suited to realistic hardware, develop the decoding algorithms that make them practical, and map the trade-offs between reliability, qubit overhead and decoding latency. A
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sovereignty, and cyber-electromagnetic resilience. The PhD researcher will primarily work within Tilburg University’s AI research infrastructure, focusing on algorithm development, model training, and
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infrastructure? Join us to develop innovative photonic technologies that enable ultra-fast, energy-efficient, and highly scalable optical interconnects for next-generation AI compute clusters. Information
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will develop cutting-edge operations research models and methods to redesign agricultural supply chains that can withstand extreme climate events and shifting agroecological conditions. Hosted by
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Python and/or C++. Experience with scientific software development is an advantage. An interest in quantum algorithms and the motivation to contribute to one of the world’s emerging research areas. Strong
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: Transparency. As part of this doctoral project, you will develop novel knowledge representation techniques, algorithms for human-in-the-loop optimization, and interactive tools that combine graphical and natural