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the esophagus with relevance to eosinophilic esophagitis. The research project will involve patient-derived samples, including organoids and air-liquid interface cultures, and experimental esophagitis models
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metagenomics, classical microbiology, and biochemistry, we study their impact on biogeochemical cycles and identify the molecular mechanisms driving these processes. Our long-term goal is to understand
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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of Neuroinformatics (http://www.ini.uzh.ch/ ) invites applications for PhD student position to work at the intersection of Neuroscience and Electrical Engineering to study neural dynamics and plasticity mechanisms
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)chemistry, biotechnology, biophysics, or a related discipline. Experience with molecular cloning, protein expression, purification, and characterization, biocatalysis, as well as high-throughput screening
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element modeling, computational fluid dynamics). Knowledge of heat and mass transport processes in heat-sensitive materials and process optimization. Experience in supply chains and hygrothermal
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Science group at ETH Zürich welcomes applications for a fully funded Ph.D. position. We utilize state-of-the-art experimental infrastructure to investigate the dynamics of matter on its natural time scales
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thoroughly analyzed with state-of-the-art theoretical models and published fully open access. The aim of our research is to advance the fundamental understanding of electrical insulation and current
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with ex situ experiments, demographic modelling or handling large datasets as well as holding a valid driver's license is a plus. Application / Contact Please upload your application via our online