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goal-oriented mindset, and a passion for (scientific) challenges, you are the right person for us. Our lab develops technologies that integrate advanced imaging, computational analysis, and single-cell
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properties of the nucleus? And do you enjoy integrating biophysics, cell biology and quantitative imaging to answer fundamental questions in biology? Then this may be the opportunity you are looking
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high-fidelity CFD–machine learning (CFD–ML) framework capable of efficiently analyzing and optimizing rooftop aerodynamic duct structures for building-integrated wind energy systems. The aim is to push
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, structural logic integration, and cross-scale design automation. Your research will contribute to digital tooling that supports scalable, flexible, and sustainable building production. You will play a key role
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changes. Scale up and spatialize model outcomes to regional and continental levels, integrating the influence of multiple environmental stressors. Where you will work Leiden University is situated between
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comparative research, helping to guide and integrate the work carried out across the project’s various work packages. More specifically, you will help establish a shared conceptual foundation for these work
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the analysis of primary material. We will aim for smooth integration of the experimental workflows and data analysis to address clinically relevant biological question in cancer immunology. We aim
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You will develop and validate advanced AI models that integrate medical imaging, multi-modal clinical and omics data, and explainable AI (XAI) for the prediction of hepatocellular carcinoma (HCC
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leverage large-scale AI models to: integrate heterogeneous EO data sources, such as satellite, aerial and in-situ data, across spatial and temporal scales; enable zero-shot or few-shot learning for rapid
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representation of real-world marine structures and their corrosion processes. By integrating comprehensive sensor measurements, experimental data on corrosion, and advanced predictive modelling (including physics