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Field
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programming (e.g., Python, MATLAB). Energy system modelling expertise with experience in academic research Preferred Skills: Educational background in Electrical Engineering, Computer Science, Renewable Energy
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programmed in advance. If anything changes, it may fail. This project explores how to build more adaptable systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural
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to validate computational fluid dynamics modelling to determine drag and vortex-induced vibrations on dSPCs associated with biofouling. Better understanding of the hydrodynamic consequences on dSPCs from key
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technology increases the grid’s exposure to cyber-attacks, which can compromise measurement signals, disrupt control commands, or induce model or data-driven instability. This project aims to develop a robust multi
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numerical modelling approach for simulating root-soil interaction, coupled with physical model testing to determine root-soil interaction parameters. The results of this phase of the project will have direct
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architectures that are impractical for scenarios constrained by limited data and resources for fine-tuning and deployment of large-scale models. What’s more, multimodal models are particularly vulnerable to data
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Theoretical models for gravitational wave signals emitted by coalescing compact binaries are the cornerstone of modern gravitational wave astrophysics. Among the most pressing challenges
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strengths and interests (e.g. geospatial data science or socio-environmental modelling). Funding Sponsored by the Leverhulme Trust and Cranfield University, this Connected Waters Leverhulme Doctoral programme
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Understanding tyre–road surface contact is critical to achieving desired vehicle performance. Friction is a complex property, unique to each system, and challenging to measure and model. To explore
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systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural language understanding (to interpret instructions), and action generation (to respond), enabling robots