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methodology will involve the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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classification algorithms including machine learning); and the output data and interpretability. The project “SORS in the community” is funded by the EPSRC (https://www.ukri.org/news/new-tools-aim-to-improve-early
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-following inverters. Implementing and optimizing scalable algorithms for transient and stability analyses on HPC architectures (CPU, GPU, hybrid). Enhancing the numerical robustness and efficiency of existing
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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experience with, statistical methods and machine learning algorithms applied to climate science research challenges. They must be highly organised and motivated and should demonstrate aptitude for programme
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world-leading collaborations. This Research Fellow will be working under the direction of Prof Cesare Tronci on the mathematical modelling of quantum-classical hydrodynamics. The interaction of classical
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of expertise for this position are: (1) Software Engineering (2) Computer Architecture and Operating System (3) Programming and Algorithms. Additionally, knowledge of Artificial Intelligence and Machine Learning
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, mathematics, data Science or related disciplines with a proven track record of expertise/expertise in tool and algorithm development and/or applied bioinformatics, who are also keen to expand our research
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should have an intrinsic interest for marketing problems. The PhD will be supervised by Prof. Dr. Lemmens and funded by a VICI NWO grant. Keywords Algorithmic bias, Causal Inference, Discrimination