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will involve a combination of longitudinal, descriptive, and cross-sectional studies to better understand the bidirectional influence of training and match-demands of tier 3 football during
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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an objective and consistent method of measurement. The implication of this is a mechanism through which customer and supplier can agree on model quality. Furthermore, the outcome will inform the development
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motion and the viewing perspective of the observer (Nikolaidis et al, 2016). This project will develop continuous models of action legibility using these sources of information from data collected in a
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an objective and consistent method of measurement. The implication of this is a mechanism through which customer and supplier can agree on model quality. Furthermore, the outcome will inform the development
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will be an advantage if candidates have an interest in optimisation, control and computing systems, model checking, mathematical logic and good programming skills, ideally in MATLAB, Python and/or C/C
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The project: The deployment of generative AI—particularly Large Language Models (LLMs) based on transformer architectures—in industrial settings poses several critical challenges. Ensuring reliable
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PhD in Electrical and Electronic Engineering: Data-driven Industrial Condition Monitoring Award Summary Mid Sweden University and Newcastle University agree to fund a PhD student project for
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no guarantee that a concurrent redundant model will return an identical result for basic cross-checking. To overcome these issues AI system developers often resort to running parallel classical-based