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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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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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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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computing, advanced machine learning applications, and the deployment of 5G and future 6G systems, the size and traffic of data centers is steadily growing. The proliferation of data centers and the need
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of the vertices in multi-dimensional space for machine learning tasks. Policy evaluation is widely used in reinforcement learning, for instance, for training large language models. Your challenge is to develop
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Fully funded (4 years) Ph.D. on: Learning Analytics-based dashboards for supporting students’ blended learning. Proposed start date is 1 September 2024, but a later start date may be considered