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Field
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. To fill in this gap, in collaboration with industrial partners, the research will develop novel Machine Learning and Computer Vision methods for detecting and localising. These will be used to develop
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: Design and implement models for a knowledge graph as part of an R&D team. Research methods and techniques for populating the knowledge graph. Develop models or algorithms to facilitate risk identification
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. The integration of AI into hardware not only enhances performance but also reduces energy consumption, addressing the growing demand for sustainable and efficient computing solutions. This PhD project delves
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of tumour growth and metastasis. Project outline The Computational Biology Group applies a range of computational and Machine Learning approaches to the interpretation and analysis of complex multimodal
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KTP Associate in Machine Learning ( Job Number: 25000811) Department of Computer Science Grade 7: - £39,105 - £43,878 per annum Fixed Term - Full Time Contract Duration: 30 months Contracted Hours
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novel sensing approaches to combine with machine learning algorithms to solve real-world problems in food manufacturing. You will have sound knowledge in electronic engineering, embedded systems design
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supercomputer in the UK and amongst the most powerful in Europe. The AI Supercomputing team owns the entire process of developing and operating the centre’s compute and software infrastructure, which includes
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(SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide enhanced interference resilience
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recordings, optogenetics, pharmacology and advanced computational tools to analyse neural algorithms, their deficits and their rescue in genetic mouse models. This project is part of a cross-species, cross
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machine vision algorithms. The system will be designed with the physical constraints of remote fusion environments in mind, including radiation tolerance, restricted access, and the need for automation and