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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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random and deterministic systems and point configurations. Potential applications include lattices, point processes, random matrices, and random walks on groups. During the project you will work
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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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. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in 2D convolutional neural networks in Python. This is a part-time position (5
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(SFDI) and also from our custom-built photoplethysmography (PPG) sensor. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in
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for Education, Research and Innovation (SERI). DC1, DC2, DC10: The PhD position will be extended to 4 years; the fourth year funded by Göteborgs Universitet, UGOT, Sweden.
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skills are key. You will be working alongside the team to analyse existing case studies, organise and run a series of interdisciplinary round tables, develop a set of resources and write reports. You will
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and innovative seminar series in London, Dehli and online, delivered by leading academics covering philosophy, creative practice, ethnomusicology and art history, which will form an integral part of