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in the network. Here unfair indicates that people with different personal traits are differently and unjustly affected by algorithms not designed to consider those traits. This project aims to develop
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unit and then pre-processed data used as the input of the deep learning algorithm. The research will employ the SafeML tool (a novel open-source safety monitoring tool) to measure the statistical
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of different faiths and beliefs. Grounded in the Christian view of human life, the KU aims to create an academic and educational culture of responsibility. The research group Reliable Machine Learning at the KU
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health. Policymakers allocate limited testing and surveillance resources across different locations, aiming to maximise the information gained about disease prevalence and incidence. This project will
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sensors systems and UAVs at different scales. In particular, we will combine borehole and surface GPR as well as small-scale EMI measurements with root and shoot observations in controlled experiments
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responsibilities include: Development of a flood classification framework for flood type prediction Comparison of different ML algorithms in a sensitivity study Communication with stakeholders Development of open
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. Design and implement multimodal unlearning techniques to address bias and privacy concerns. Evaluate the generalisability of multimodal learning across different socio-contexts. Validate the proposed
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simulations are plagued by the same slow relaxational dynamics. Through collaboration across Engineering, Statistics and Chemistry, this project will develop state-of-the-art simulation algorithms to circumvent
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behavioral component as it would be interesting to study how consumers perceive algorithmic discrimination of different kinds and how companies can mitigate negative perceptions. During their PhD
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CFD technologies. As the PhD researcher on this project, you will investigate and develop the numerical and algorithmic components needed to make this hybrid high order to low order strategy practical