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economics and seeking hands-on research experience before graduate school. Research Areas • Tariffs and trade policy • Stablecoins and digital currencies • Optimal policy design in open economies • Broader
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) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and
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SPHERE You have a PhD degree in Artificial Intelligence, Computer Science, Applied Mathematics or in a related field You have at least 3 years of postdoctoral research experience or R&D experience in
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) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and
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/funding reference: PID2021-124030NB-C31 Job title: Optimization, learning, and cooperation with applications in logistics Research line / Scientific-technical services: Optimization in transportation and
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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the attached job pack. Duties of the Role You will undertake a PhD at the University of Essex and deliver DC15: “Nature-Inspired Optimization Strategies for Quantum Network Routing: Leveraging Decoherence
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optimization of the trade-off between accuracy and speed: measurement of throughput (labels/hour), uncertainty/confidence thresholds, human-in-the-loop strategies, cost/benefit considerations for different data
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for food, feed, and fiber handling and storage. Apply mathematical modeling and scientific principles to optimize operational performance. Collaborate with faculty, graduate students, and industry partners
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MSc in applied mathematics or engineering Strong analytical and numerical skills Knowledge of optimization and operations management Knowledge of programming, possibly Python Preferred knowledge