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Field
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. Expect close collaboration with industrial experts and the opportunity to see your algorithms influence aerospace and other high-value manufacturing sectors. Funding and eligibility 3-year, full-time PhD
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in Physics, Mathematics, Computer Science or a closely related field, ideally with a strong focus on quantum computing and numerical simulations. • Experience in data-reuploading quantum algorithms
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. Expect close collaboration with industrial experts and the opportunity to see your algorithms influence aerospace and other high-value manufacturing sectors. Funding and eligibility 3-year, full-time PhD
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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graph learning models, primarily geared towards assisting combinatorial solvers for practical graph algorithm benchmarks. Please find out more here: Dr. G. Rattan Information and application Are you
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, led by Orange Labs, whose objective is to design and implement efficient algorithms for computing optimal solutions in order to recommend the best actions a telecommunication operator can undertake for
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between things, developing the mathematical and computational foundations for optimal information transfer between surfaces and volumetric current distributions in complex scattering media. The research
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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. You will be working at the intersection of battery modelling and algorithm development, with a strong emphasis on the data-driven modelling and control aspects. You will contribute to shaping
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management of microgrids, battery scheduling, and handling uncertain renewable generation without relying on forecasted data [1]-[5]. Studies reveal that parametric tuning of RL algorithms such as state and