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developing the theoretical and algorithmic foundations of compositional world models. A key application focus of the grant lies in rapid and safe real-world skill acquisition in application domains such as
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University of London. Building on ongoing work in the group on the evolution of eukaryotic DNA methylation (www.demendozalab.com ), the project will focus on the comparative and evolutionary analysis of DNA
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algorithms for entanglement distribution in quantum networks. The purpose of the role is to contribute to the project "Utility Optimization in Quantum Networks: Algorithm Design and Analysis", working with Dr
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transcriptomic data and an interest in evolutionary biology and conservation are highly desirable. Candidates must hold a PhD (or equivalent) in conservation genomics, evolutionary biology, or a closely related
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algorithms for entanglement distribution in quantum networks. The purpose of the role is to contribute to the project “Utility Optimization in Quantum Networks: Algorithm Design and Analysis”, working with Dr
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About the Role The position is funded through the EPSRC project “Zeros, Algorithms, and Correlation for graph polynomials”. We study various combinatorially defined polynomials such as the
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on the design and performance analysis of resource allocation algorithms for entanglement distribution in quantum networks. The purpose of the role is to contribute to the project "Utility Optimization
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Computer Science, Chemistry, Chemical Engineering, Physics, or Materials Science. You will develop optimisation and machine-learning algorithms for human- and literature-informed discovery of new materials
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Computer Science, Chemistry, Chemical Engineering, Physics, or Materials Science. You will develop optimisation and machine-learning algorithms for human- and literature-informed discovery of new materials
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models and algorithms, particularly within Bayesian, generative, or probabilistic machine learning frameworks, together with deep knowledge of causal inference, prognostic modelling, and individualized