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Probabilistic Circuits. Causal Representation Learning. Causal Explanations. Causality and Large Language Models. Counterfactual learning. Job requirements Master’s degree in Computer Science, Mathematics, or a
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motivated and skilled PhD candidate to work in the area of probabilistic machine learning. The position is fully funded for a term of four years. The research direction will be determined together
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computing (SC)? Are you fascinated by the emerging field of machine learning (ML)? Are you our next PhD-candidate in scientific machine learning or SciML (combining SC and ML)? Are you eager to work on the
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models. Trustworthy AI is a major topic in machine learning, which is illustrated by the increasing number of initiatives to enforce AI systems to be more trustworthy. Although machine learning models
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Irène Curie Fellowship No Department(s) Applied Physics and Science Education Reference number V34.7526 Job description Are you inspired by combining physics-based models with machine learning
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) collaborating with machine learning experts (a second PhD student in USA) integrating personalised AI into meaningful assistive technology interventions (4) critically evaluating methodological approaches in
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of machine capabilities. However, learning these models requires direct access to vast data repositories, which poses significant privacy and logistical challenges, especially in the health sensing domain
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humanities on AI safety and explainability. Reviewing technical literature on machine learning and explainable AI. Developing a normative evaluation framework for the use of explainable AI in machine vision
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machines are commonly used in electric vehicles. However, these machines have several disadvantages, such as the high cost of the permanent-magnet (PM) material and decreased performance at higher
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machine learning; a minor in mathematics is a plus; A strong interest in data mining research with focus on local pattern mining, exceptional model mining, or related techniques. Data mining software