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computational methods for modelling social dilemmas that can account for real-world complexity in agents’ behaviour. We will build on novel computational techniques to produce realistic enough models that can be
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would develop new computational methods for modelling Groupthink that can account for real-world complexity in agents’ behaviour, and build realistic enough models that can fit past and present empirical
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systems and swarm robotics. The project builds on well established computational and mathematical modelling techniques to achieve its aims. Departure points will be agent-based simulations, optimisation
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Many machine learning (ML) approaches have been applied to biomedical data but without substantial applications due to the poor interpretability of models. Although ML approaches have shown
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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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privacy-enhancing techniques such as secure multi-party computation, homomorphic encryption, differential privacy, and trusted execution to design algorithms and protocols to secure ML models within
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that occurs within these biological neural networks, so that these networks can be leveraged for AI applications. In addition, you will develop mathematical and computational neuroscience models
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Artificial Intelligence (AI) models are widely used in decision making procedures in many real-world applications across important areas such as finance, healthcare, education, and safety critical
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analysis, contextual analysis, audio feature extraction, and machine learning models to identify and assess potentially dangerous content. Similarly, computer vision models are implemented to analyse images
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of Decision Trees, Support Vector Machines and Logistic Regression Models based on Pre-Computation", IEEE Transactions on Dependable and Secure Computing 2019 #digitalhealth