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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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multimodal systems. The emphasis is on agentic approaches, where an LLM interacts with visual tools, which may themselves be neural networks. Central challenges include enabling LLMs to reason about visual
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on agentic approaches, where an LLM interacts with visual tools, which may themselves be neural networks. Central challenges include enabling LLMs to reason about visual structures, designing
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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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robotic systems and AI models. You will learn how to programme advanced robotic systems and how to implement aspects of deep learning and neural networks for chemical property prediction. You will be part
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cooperation with Kopter Germany GmbH and the Engineering Risk Analysis Group of Prof. Straub, which provides information on both the health and the actual stress of helicopter components. For this so-called
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architectures which leverage our increasing understanding of the behaviour of neural networks trained with DP to ameliorate these trade-offs in biomedical applications. - Foundations of private machine learning