980 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" research jobs in United Kingdom
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to £43,805 per annum Apply by: 26/04/2026 Role Description We welcome applications from skilled, delivery-focused engineers to contribute to our successful railways-applied smart machines research programme
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-focused engineers to contribute to our successful railways-applied smart machines research programme. About the Role We are seeking a highly motivated and multiskilled Research Fellow with expertise in
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to £43,805 per annum We welcome applications from skilled, delivery-focused engineers to contribute to our successful railways-applied smart machines research programme. About the Role We are seeking a highly
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second in the UK for research power and first in England. About the role The project will be carried out at the Department of Computer Science, in the Machine Intelligence Lab (https
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, cardiovascular diseases and rare diseases. This specific role focuses on engineering CAR-T cells and developing programmable gene control systems to improve safety and therapeutic efficacy. The successful
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personal and professional development opportunities. To learn more follow the link to our benefits website . To apply For further information on the Post Doctoral Fellowship Schemes and how to apply please
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this complex and challenging project as opportunities allow. Lab Website: https://ilandavis.com/ Informal enquiries should be directed to Professor Ilan Davis, Ilan.Davis@glasgow.ac.uk This post is part-time
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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design and implementation of visual cues detection on video datasets (face and gesture), context-aware multimodal analysis, machine learning/self-supervised learning for automatically detecting subtle
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Tiny Machine Learning (TinyML). The role will focus on the design and development of battery‑less, ultra‑low‑power IoT systems capable of executing secure TinyML‑based visual perception algorithms