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methodology to better understand the safety and performance risks. Finally, multiscale simulations will be used to map learnings from laboratory-based systems (up to10 kW) to predict the behaviour and
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established methods of microstructural analysis and mechanical testing with new schemes such as Acoustic Emission for non-destructive assessment of degradation and Machine Learning for development of predictive
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established polymers with novel biodegradable entities with appropriate performance and cost is a significant challenge, requiring the ability to predict whether candidate molecules would be broken down by
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 2 months ago
: Analyse how curation and drying impact mechanical properties and hygrothermal performance. Develop a Predictive Model: Create a computational model linking operational variables and material properties
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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, tasked with microstructure characterisation to inform the model and with performance testing to validate the model predictions. Together, the team will evaluate the empirically and computationally obtained
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systems. There are virtually no satisfactory ways of exhaustively ensuring and demonstrating that these stochastic systems meet the demonstrable, repeatable, and predictable expectations of existing safety
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. The subsequent data will then be used to populate machine learning models to predict which molecules to synthesise next, to maximise the binding affinity of the molecules to a target protein. This research aims
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. This project aims to establish provable guarantees for Human-GenAI-Alignment by integrating statistical methods with adversarial methods. For example, by leveraging PAC methods and conformal prediction, we can
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building typologies. This research aims to transform Pulse testing through AI integration—specifically leveraging descriptive, predictive, and generative modelling techniques—to enhance test accuracy