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innovative approaches to enhance privacy in ML models, algorithms, and workflows, with a particular emphasis on preserving confidentiality while maintaining the utility and accuracy of the learned models
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of mobile ringtones. Traditional machine learning methods and transformer models will be used to learn patterns from audio signals and classify ringtones into predefined categories (e.g., default ringtones
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. They generally rely on expert rules or machine learning models to provide health advice. Recently, generative AI tools, such as ChatGPT, have become a popular focus of research. In healthcare, they show strong
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this work we will use Theory of Mind (ToM) to reason about the mental models of the actors involved in AI systems, designers, agents and users and expand on the "Model Reconciliation Problem" or reconciling
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This project involves model-based depth of anaesthesia monitoring using autoregressive moving average modelling and neural mass and neural field modelling of the electroencephalographic (EEG) signal
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, decision-making processes, and even democratic institutions. Large language models (LLMs) have shown tremendous potential in natural language understanding and generation. This research aims to harness
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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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In many branches of science (e.g., Artificial Intelligence, Engineering etc.), the modelling of the problem is done through the use of functions (e.g., f(x) = y). On a very high-level, we can think
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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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, software, human-computer interaction, ...). We also work very much interdisciplinarily with colleagues from other faculties, e.g. on bio-diversity matters, on physical aspects, on modelling aspects, and on