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at the intersection of machine learning, bioinformatics, and computational pathology. Project Overview: Integrating histopathological imaging with omics (e.g., transcriptomics, genomics, proteomics) holds tremendous
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This PhD project aims to advance Safe and Sustainable by Design (SSbD) pharmaceutical manufacturing by integrating cutting-edge methodologies, including computer-assisted retrosynthesis, end-to-end
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This large-scale ecological project investigates the barriers and drivers of post-fire forest recovery. With climate change and the spread of forest fires to new areas, it is important to
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
artificial intelligence, machine learning, data analysis, or digital systems would be advantageous but is not essential. We value curiosity, problem-solving ability, and a proactive attitude toward learning
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of good-quality data is typically limited for high-value critical assets. This PhD project will focus on developing, evaluating, and demonstrating physics-informed machine learning or domain knowledge
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nonlinear effects. These nonlinear effects will be generalised via correction terms discovered by machine learning from a large numerical simulated dataset. This dataset also allows for extending the theory
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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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, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will have experience in one or more of these subject
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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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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will