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To support the UrbanHeatMap project, which aims to develop scalable, high-resolution ambient temperature mapping methods by integrating outdoor microclimate measurements, mobile sensing data, satellite-derived land surface temperature, and urban morphology information. The Research Assistant...
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air temperature maps • Develop and train Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) architectures to model complex spatial and temporal dependencies in urban microclimate data
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Networks (GNNs), Convolutional Neural Networks (CNNs), and Transformers. These resources will be made available to researchers across different scientific domains. Ultimately, this initiative will empower
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the correctness, robustness and reliability of deep neural networks and AI-enabled software systems. Job Responsibilities: Conduct research in adversarial machine learning, AI security and the robustness of deep
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the correctness, robustness and reliability of deep neural networks and AI-enabled software systems. Job Responsibilities: Develop novel methods and algorithms for the verification, testing and robustness analysis
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the neural basis of flexible cognition. From basic functional circuit dissection in animals to human clinical studies, we ask how network disruptions cause cognitive deficits in neuropsychiatric disorders like
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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow – Human Neuroimmunology, Brain Organoids and Multi-omics Department MS Research Network | Department of Medicine
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at least one of the following fields: approximation of neural networks, convergence of Langevin/MC sampling, analysis and algorithm design of interacting particle systems, operator learning, regularity
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, bioRxiv 2026, doi: 10.64898/2026.07.30.741759) for biological systems. You will lead a computational research project extending DMMSs from population-level to single-cell data, combining neural networks
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. Additionally, we intend to measure root water uptake using sap flow meters. The data will be integrated using recently developed physics-informed neural networks in order to translate apparent resistivity data