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recently completed a pilot study investigating the use of atmospheric pressure ionisation mass spectrometry coupled with machine learning for differentiating between brain tumours and normal tissue
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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artificial intelligence/machine learning may be applied are encouraged to apply. If you are interested in research in brain-machine interfaces, and are unsure about whether you have the right background
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-based transfer learning classification model for two-class motor imagery brain-computer interface. International Journal of Neural Systems (IJNS). https://doi.org/10.1142/S0129065719500254 * Kudithipudi
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manufacturing sectors. Applicants should have a good background in mechanical, energy, or related engineering disciplines, with knowledge of thermal-fluid sciences, and be familiar with or willing to learn
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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between the brain signals of different subjects. The aim of this project is developing new adaptive and machine learning algorithms to successfully decode brain signals across subjects. The prospective
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website areas. Without these cookies, the website would not be able to work properly. Service: Cloudflare Purpose: Pending Privacy policy: https://www.cloudflare.com/privacypolicy/ Expiry: Session Name
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modelling, multimodal neuro-imaging and physics-informed machine learning to improve assessment of glioblastoma treatment response. The candidate will also be expected to contribute to the formulation and
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machine learning methods to model changes in the brain over the lifespan, including brain structure and function, and how those changes relate to environment and genomics. About the Role The post is funded