51 distributed-algorithm-"Newcastle-University"-"Newcastle-University" Fellowship positions in United Kingdom
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of distributed characterization of hollow core fibres, including properties such as attenuation, polarization, but also pressure and content of air inside the fibres. The successful candidate will be based
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of distributed characterization of hollow core fibres, including properties such as attenuation, polarization, but also pressure and content of air inside the fibres. The successful candidate will be based
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of distributed characterization of hollow core fibres, including properties such as attenuation, polarization, but also pressure and content of air inside the fibres. The successful candidate will be based
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numerical algorithms or high-performance software for scientific computing? Do you have expertise in numerical linear algebra or experience with large-scale computing and want to broaden your skills? Would
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successful candidate will primarily work on the development and enhancement of the OASIS model, contributing to new physical parameterisations, numerical algorithms, and/or performance optimisation. They will
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Electronic Engineering, Control Engineering, Computer Science or a very closely related topic: Strong understanding of power electronics principles Excellent knowledge on data-driven machine learning algorithm
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computers”(under the UKRI Guarantee scheme). Topics include: - Quantum many-body dynamics - Quantum algorithms - Quantum-enhanced numerical methods - Quantum machine learning - Tensor Networks - Topological
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successful candidate will primarily work on the development and enhancement of the OASIS model, contributing to new physical parameterisations, numerical algorithms, and/or performance optimisation. They will
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. This role will contribute the development of novel machine learning algorithms for wireless sensing and communication and the proof of concepts for next-generation wireless communication, collaborating with
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decoherence dynamics and other effects of quantum-classical correlations in complex fluid solvents. The methodology will be largely based on Lie group methods and structure-preserving algorithms in geometric