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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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the design and analysis of such models. PhD position 1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD
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? Then check out the vacancy below and apply for a PhD position in this exciting research direction. Join Us! Modern deep learning is progressing fast. Yet even the most advanced neural networks are paired
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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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exciting research direction. Join Us! Modern deep learning is progressing fast. Yet even the most advanced neural networks are paired with crucial limitations, such as making arbitrarily bad predictions
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. Investigate and implement optimum sampling strategies, including sparse and compressed sampling techniques. Explore the applicability of neural networks in clinical workflows, ensuring solutions are practical
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evolutionary procesess have shaped the organization of human cognitive brain networks and their cognitive functions and vulnerability to mental conditions. By applying neuroimaging, genetics and network
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families (e.g., generative models or graph/equivariant neural networks) to accelerate candidate discovery and hypothesis generation. Disseminate research findings through publications, conference
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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
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, such as physics-informed neural networks (PINNs), and apply them to regenerative processes. Collaborative by nature – You enjoy working across disciplines and feel at ease in an international