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for understanding their reliability and for making informed decisions based on their predictions. This project aims to develop new methods for uncertainty quantification in mathematical and statistical models
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application! We are looking for a PhD student in Medical Science, AI and Bioinformatics. Your work assignments This project aims to develop AI foundation models for integrative single-cell and multi-omics
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patients develop a durable response. Many researchers are investing efforts to understand the complexity of anti-cancer immunity and develop diagnostic approaches that accurately predict therapy benefit and
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prediction, most current models still describe proteins largely as static structures and do not fully capture the conformational ensembles that underlie protein function. This PhD project aims to address
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predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming, mathematics, physics. You will
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in predictions derived from medical reports, and on integrating these uncertainties into downstream probabilistic time-to-event models. Applications will focus on prostate cancer, using large-scale
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of cohort analysis, prediction modelling, or machine learning techniques • Good knowledge about pancreatic cancer epidemiology • Excel in R or SAS • Good publication records Priority will be given