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This PhD project will focus on developing, evaluating, and demonstrating a framework of novel hybrid prognostics solution for selected system use case (e.g. clogging filter, linear actuator, lithium
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approaches (e.g. SPG) as well as the use of machine learning, advanced computing, statistical modelling to explore the stochastic response to complex scenarios. This project offers the opportunity to undertake
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progression to PhD depending on performance during the initial period. Main duties and responsibilities Admin - ordering consumables, tracking cost and use of consumables PIL training - opportunity to acquire a
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internships An additional disability allowance can be provided where appropriate Students opting to study part-time will receive a pro-rata maintenance stipend. Specified use +3/+4 - covering a full PhD
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learning. The project involves a collaborative team, including a postdoctoral researcher and a PhD student, with specific objectives: Define and acquire a comprehensive database of high-quality video priors
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one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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of the PhD student extending the overall project to 4.5 years. About HetSys The EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems (HetSys), based at the University of Warwick, is an
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and trends in wall adhesion. Through Syngenta’s involvement, we will have access to experimental to guide the project inform modelling decisions. Project Objectives for the PhD project The aim
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, mathematics, statistics and engineering backgrounds who are passionate about applying their mathematical expertise to tackle complex, real-world problems. By fostering these skills, HetSys trains the next