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Field
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climate and market “shocks”. We will gain a deep insight into assessment methodology, as well as identify opportunities/barriers for sustainable and resilient biomass production in future landscapes. A
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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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may also explore embedding these new computational methods into optimisation and machine learning contexts. The new computational techniques developed will be geared towards the following key
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. Fe, S) on CNT purity and structure. Evaluate CNTs as conductive additives in standard Li-ion battery electrodes. Apply AI/machine learning to optimise experimental design and growth parameters
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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motivated candidate with an interest in building quantum technology and learning a wide range of skills in optical systems, precision measurements, electronic control and feedback systems, vacuum systems
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learning’ approaches (such as Deep CNN’s) and ‘unsupervised learning’ approaches (such as reinforcement based learning and generative adversarial networks). Some of the main problems with Second Wave AI
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Characterisation" "Data Science and Machine Learning in Materials" "Plastics Recycling and Circular Economy" Research theme: "Materials Characterisation" "Data Science and Machine Learning in Materials" "Plastics
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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electrolyser devices. However, we do not expect you to have prior knowledge of fabricating and running electrolysers or computational models. You will get the opportunity to learn from both the experienced