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relevant Masters qualification in an appropriate subject (e.g. Psychology, Neuroscience, Neuro-engineering, or related fields). Experience with (or a strong interest to learn) computer programming is highly
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, manipulate large datasets, visualise data and perform numerical and statistical analysis is a requirement. Experience in handling 'big data', machine learning and working in distributed teams, is useful
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to test data, which are therefore only useable within a narrow range of scenarios. These limitations result in the requirement of large number of high-cost experiments being conducted to populate the models
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experimentation and validation, and machine learning. References of our current/recent work are here: "Automatic Retrieval-Augmented Generation of 6G Network Specifications for Use Cases," IEEE Communications
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of (or aptitude to learn) quantitative data analysis and coding (e.g. R). Or a background in computer or data science who can demonstrate their ecological or natural history knowledge. Candidates should have a
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to develop forecasting models. The use of machine learning methods for demand modelling could also be considered. The models that are developed will be implemented in a modelling tool which could be used by
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algorithms, have excelled in tasks like computer vision, image recognition and large language models (LLM). However, their reliance on extensive computational resources results in excessively high energy
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engineering or a relevant area. An MSc degree and/or experience and good knowledge in gas turbine theory, thermodynamics, Machine Learning, and computer programming will be an advantage. Funding Sponsored by
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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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. Alternative approaches are graph-based molecule reaction space sampling and generative machine learning as they provide a path to new synthetic data that can form the basis for a large-scale database of