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methods for evaluating intelligence in people are not suitable for AI and vice versa due to inherent differences in learning, memory, and processing between these systems. This project develops
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to the development of innovative, physiology/ machine learning-driven clinical solutions and decision support tools for critically ill patients, focusing on cardiovascular and respiratory monitoring, mechanical
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organizational skills. Demonstrable experience with using machine learning packages (e.g., PyTorch). Completed academic courses in AI or machine learning. We consider it an advantage if you bring experience with
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, the identification of predictive features, and the construction and validation of statistical or machine-learning-based models. The postdoctoral researcher will be responsible for: Developing a
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systems increasingly provide personalized recommendations in domains such as nutrition and lifestyle. However, many recommender and prediction systems rely heavily on opaque machine learning techniques
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. You will combine technical work on machine learning with qualitative analysis of how AI systems are interpreted and used in organisational decision-making. Join the Human-Centred Computing group
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, multi-modal data, and GPU-accelerated machine learning for materials science. Information We are seeking two highly motivated postdoctoral researchers to join the Horizon Europe project SIMU-LINGUA, a
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supervision signals (e.g., labels in a downstream task or symbolic constraints). You will perform machine learning research, developing a framework for learning interpretable and robust concepts with
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(such as satellite video or hyper-spectral imagers), new processing methods (such as inverse SAR, microdoppler, multi-data fusion or machine learning-based pattern recognition) and new opportunities
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people with concerning behaviour. The ZonMw funded project “REaL-VR (Reflective Experiential Learning in Virtual Reality)” aims to deliver novel training technology for first responders (police, community