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Field
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role of neural rhuthms for inter-area brain network communicartion PHD2: The neural code for multi-item representation in working memory PHD 3: The dynamic interplay between brain and bodily rhythms in
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of this annex, as well as to: Programming in Python and R. Statistical classification and machine learning methods: SVM, neural networks and logistic regression. 3.2. Qualification: Official Master’s degree in
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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are recruiting three PhD students with distinct research foci: PHD 1: The functional role of neural rhuthms for inter-area brain network communicartion PHD2: The neural code for multi-item representation in
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neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection. Comparison with known analytic methods and
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knowledge of wireless communications, and signal processing. You have at least intermediary knowledge of machine learning algorithms, including federated learning, split learning, and graph neural networks
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methods. Specifically, brain samples will be rendered transparent with optical tissue clearing methods and imaged with 3D microscopy techniques, particularly light-sheet microscopy. The vascular network
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and neural networks for chemical property prediction. You will be part of the Big Chemistry consortium and will also be involved in training and teaching BSc and MSc students (10% of your working time
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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operational employment. This doctoral research will thus leverage the power of graph neural networks – a novel ML architecture, capable of learning fundamental physical behaviour by modelling systems as graphs