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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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physics-based theory with data-driven models, you will contribute to the next generation of predictive tools for materials design and discovery. You will also collaborate closely with experimental and
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data. Perform numerical simulations of subsurface hydrogen storage including chemical reactions for an ensemble of geological models that were developed in the Rapid Reservoir Modelling software. Develop
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imaging, multi-modal clinical and omics data, and explainable AI (XAI) for the prediction of hepatocellular carcinoma (HCC). Your research will directly contribute to early detection and risk stratification
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omics data, and explainable AI (XAI) for the prediction of hepatocellular carcinoma (HCC). Your research will directly contribute to early detection and risk stratification in patients with liver disease
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this project is how to effectively prioritize the millions of unknown biosynthetic gene clusters and metabolite features for the discovery of new antimicrobials through predicting structural and functional
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this project is how to effectively prioritize the millions of unknown biosynthetic gene clusters and metabolite features for the discovery of new antimicrobials through predicting structural and functional
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of Science at UvA. What are you going to do? The aim of the project is to use advanced Machine Learning techniques to predict the anharmonic vibrational spectra of large Polycyclic Aromatic Hydrocarbon (PAH
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perovskites — for applications in sustainable energy, spintronics, and quantum technologies. By combining physics-based theory with data-driven models, you will contribute to the next generation of predictive
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of Science at UvA. What are you going to do? The aim of the project is to use advanced Machine Learning techniques to predict the anharmonic vibrational spectra of large Polycyclic Aromatic Hydrocarbon (PAH