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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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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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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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, 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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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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expertise in advanced econometric techniques, data modelling, and statistical analysis. The Master of Applied Econometrics program combines rigorous coursework with hands-on experience, equipping you with
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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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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
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optimisation models (e.g. using MiniZinc) and solving methods to demonstrate the transformative potential of optimisation technology in this application. Collaboration will be key, as you’ll join the DSAI’s