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, molecular communication, and systemic molecular networks, and to identify how these processes change across physiological and disease states. Through this work, you will develop predictive and biologically
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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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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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project 1. Only 2% of the human genome sequence codes for proteins, while most of it consists of noncoding sequences, including regulatory factor binding regions, transposable elements, pseudogenes
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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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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
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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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 reproducible code through code sharing platforms, version control, workflow languages and container solutions. Documented ability of responsible and legally compliant adoption of machine learning/AI methods