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multiple brain areas in an optimised way. This may include neural network and neural mass modelling of large-scale brain activity during and after stimulation, and experimental tACS in healthy participants
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sciences or a related field, with practical experience in molecular and cellular techniques, preferably including work with iPSCs and iPSC-derived neural or myogenic cell types. You have a strong passion for
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science, environmental modelling, geosciences, or related field with strong quantitative focus; Strong background in machine learning methods such as neural networks and transformers; Knowledge on handling
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spaceborne remote sensing. You will first identify large-scale drivers of compound extremes in models and observations, then build an emulator using advanced AI methods, such as convolutional neural networks
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1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD position 2 will focus on designing scalable
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programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. In particular, you will be part of the Causality team under the supervision
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is embedded in an international academic–industrial collaboration and targets fundamental questions in end-to-end autonomous driving and neural view synthesis. Your work is expected to lead to
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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering