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Machine Learning techniques to this data to extract the essential information contained within these trajectories. This will be achieved through the following steps: Develop tools to efficiently generate a
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Project title: Machine Learning models for subgrid scales in turbulent reacting flows Supervisory Team: Temistocle Grenga, Ed Richardson Project description: Supervised deep convolutional neural
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31st July 2024 Languages English English English The Department of Engineering Cybernetics has a vacancy for a PhD Candidate PhD Candidate in Hybrid Machine Learning Apply for this job See
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, develop methods to speed up this development by focusing on machine learning techniques to address sustainability challenges in chemistry research. We will explore novel pathways to process design and
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University of Technology (TU/e)CountryNetherlandsCityEindhovenPostal Code5612 APStreetDe Rondom 70Geofield Where to apply Website https://www.academictransfer.com/en/342216/phd-position-in-causal-machine-learn
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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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machine-learning techniques in ST studies. Our approach introduces two innovations: developing sparse Bayesian learning algorithms for efficient small dataset analysis and designing a simulator for
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, scalability, interpretability, and explainability of AI. The UAI group is looking for a highly motivated and skilled PhD candidate to work in the area of probabilistic machine learning. The position is fully
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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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the development of artificial systems that each integrate different aspects of machine learning, multimodal sensing, ubiquitous computing and social science. In addition, the successful candidates will have the