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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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here: Demonstration of Deterministic QC with one clean qubit (DQC1) on a neutral atom array How to apply If you would like to apply then please read the guidance on applying for a PhD studentship here
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and deterministic AI outputs is critical. This requires robust design principles and architectural changes to reduce variability and integrate smoothly with industrial control systems. Enhancing
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. A non-deterministic AI machine learning model for the identical task would not offer this demonstrability or, critically, the repeatability of classical algorithm-based systems. Furthermore, there is
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: Diagnosis, RehabilitatiOn & Prognosis). The Doctoral Researcher (DR) will join a cohort of DRs who will be working on a series of interlinked, interdisciplinary projects for sustainable, intelligent, and
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. This project will investigate how such oscillations can be mitigated using series connected flexible AC transmission (FACTS) devices. The project will be carried out in close collaboration with GE Vernova
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: Diagnosis, RehabilitatiOn & Prognosis). The Doctoral Researcher (DR) will join a cohort of DRs who will be working on a series of interlinked, interdisciplinary projects for sustainable, intelligent, and
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, producing often complex financial models as required and providing insightful guidance on financial structures, processes and regulations. They will work closely with colleagues in MPLS Division on long and
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such as landslide movement style, runout, and how landslide hazards evolve over time. This Ph.D. project will leverage the analysis of new time-series data from cloud-based satellite image archives