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networks and machine learning strategies for the analysis of scattering data. Large amount of scattering data obtained in our group requires development of the advanced analysis techniques. In this project
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group has implemented state-of-the-art deep learning for underwater communications; deep learning models underwater environment based on real data. Our preliminary study shows that state-of-the-art deep
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. (2) Prof. Francesca Grisoni (https://molecularmachinelearning.com/ ) leads the Molecular Machine Learning Group at the Technical University Eindhoven and will lead a project on designing potent cyclic
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Supervisors: Prof. Gabriele Sosso, Dr Lukasz Figiel, Prof. James Kermode Project Partner: AWE-NST This project utilises advancing machine learning techniques for simulating gas transport in
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polis. At Groningen University, you will be offered a unique opportunity to work in an international environment and to acquire valuable research experience. The PhD Project Life in the Greek-speaking
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and to acquire valuable research experience. The PhD Project Life in the Greek-speaking cities of the Eastern half of the Roman Empire was infused with cultural interactions and the rich and pluralistic
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. Alternative approaches are graph-based molecule reaction space sampling and generative machine learning as they provide a path to new synthetic data that can form the basis for a large-scale database of
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are looking for a highly motivated and skilled PhD researcher to work on graph-based machine learning surrogates of wind energy systems. Our goal is to accelerate flexible fatigue load estimation
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to data-driven research projects focused on glaucoma, diabetes or nAMD using large-scale clinical and imaging datasets. Develop, validate, and implement machine learning models and statistical tools
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(crowding, concentration of nutrients, size of liposome, etc.) with gene expression levels across a synthetic genome. To achieve this aim, you will use VASA-seq and Ribo-Seq to generate large data sets