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-22 eV or better, and powerfully test the Standard Model of particle physics. They further constrain CP-violating new physics at scales of 10-100 TeV, far beyond the reach of the LHC. The TUM and the
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. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
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for modelling infection including macrophages, organoids, zebrafish, primary and clinical samples. Embedding within the Institutes of Biology and Chemistry at Leiden will enable the candidate to maximize
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benefits through our Terms of Employment Options Model. In this way, we encourage our employees to continue to invest in their growth. For more information, please visit Working at Utrecht University
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and cultural activities external link . We also offer access to additional employee benefits through our Terms of Employment Options Model. In this way, we encourage our employees to continue to invest
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biological data, development of deep learning and large language models for biological discovery or graph-based methods for molecular and cellular networks. The technological foundation further consists
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research focus will include some of the following topics: Advanced sensor fusion and multimodal AI models for robotic intercropping. Self-supervised learning will generate multimodal agricultural pre-trained
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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friendly catalysts and substrates. We use NMR spectroscopy for serendipitous discoveries and unbiased characterizations of molecules, their conversion, and their interactions in complex systems. Solvent
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interfaces. Topics of interest include: Planar and geometric graph algorithms Approximation and parameterized algorithms Clustering, embeddings, and structural graph theory Computational complexity and