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Field
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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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 investigate the conditions that support forest recovery after a fire. The study areas can be defined using...
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the form of a human-expert informed reward function. Second, we aim for the integration of low-energy machine learning algorithms, so that the resulting AI model can run on a variety of devices, including
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machine learning algorithms and to assess when AI predictions are likely to be correct and when, for example, first principles quantum chemical calculations might be helpful. Predicting chemical reactivity
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machine learning algorithms and to assess when AI predictions are likely to be correct and when, for example, first principles quantum chemical calculations might be helpful. Predicting chemical reactivity
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part of the larger project. The scope of the PhD project is to implement, use, and where required develop, statistical machine learning tools to identify DNA mutations that cause particular types
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is to develop a highly innovative ‘Lab on a Bench (LoB)’ setup, integrated with Machine Learning algorithm, as a high throughput method for screening and developing formulated products that are used in
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; Midlands Graduate School Doctoral Training Partnership | Nottingham, England | United Kingdom | 3 months ago
the consumption journey. This exciting PhD Programme will look to address these challenges by employing a novel combination of data-donation, data-enhancement, and data-augmented laboratory experiments. The project
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Supervisors: Prof. Gabriele Sosso, Dr Lukasz Figiel, Prof. James Kermode Project Partner: AWE-NST This project utilises advancing machine learning techniques for simulating gas transport in
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optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in