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Field
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sensor data in a materials science context. An excellent record of productive and creative research demonstrated by publications in peer-reviewed journal papers. Excellent written and oral
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particular novel hollow-core fibres for applications in high-speed commu-nications, quantum technology, sensor technology, etc. Publication of the achieved results in scientific journals and at conferences
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NSF-EPSRC project “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks”. The research will involve collaboration with
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pavement networks. This position will focus on modeling and optimizing pavement performance under evolving climate and hazard conditions. This position will advance modeling of pavement life-cycle
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, reliability, and consistent behavior. Learning-based controllers can achieve high performance in complex and uncertain environments, yet ensuring predictable operation under distribution shifts, sensor noise
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important pathogens. About you The successful candidate will develop and apply genome analysis pipelines to identify potentiating mutations and/or gene gains on projects across the IOI and PubMLST network
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(TMDH) project that aims to integrate NASA Earth observation (EO) products, in situ and field-sensor records, and advanced hydro-geologic models into an operational platform to support U.S.–Mexico treaty
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network and is a research-intensive department. We expect academic excellence and offer an inspiring and stimulating working environment, flexible working hours and excellent opportunities for professional
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: Ph.D. in Physics, Computer Science or closely related areas Preferred: Demonstrated strength in one or more of the following. - Quantum many-body numerics: tensor-network methods, variational Monte Carlo
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, from genes and molecules to brain networks and psychosocial systems”, PN-IV-P6-6.1-CoEx-2024-0139, part-time / full-time, in accordance with the project funding contract and the activity implementation