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group has expertise in radiology, biostatistics and machine learning. We have a strong collaboration with research groups at KTH, at KI, and internationally within two EU projects and a new collaboration
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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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of robustness, safety, trust, reliability, tractability, 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
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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