Uppsala University is seeking a PhD student in Causal Machine Learning for Precision Medicine, part of the interdisciplinary WASP-DDLS NEST project AID4BC.
Project description
his PhD project is part of the interdisciplinary WASP-DDLS NEST project AID4BC, which has the overarching aim of advancing data-driven multimodal methods to enable true precision diagnostics throughout the breast cancer pathway. AID4BC’s constellation of partners, located at four leading sites in Sweden, is likely the only constellation globally having access to large (>10,000 patients) matched multimodal data across radiology, pathology and molecular profiling and clinical data.
Machine learning methods hold the potential to advance precision medicine and clinical decision support for breast cancer diagnostics and treatment. At the same time, these application areas present new challenges for statistical learning methodology. They involve high-stakes decisions with important trade-offs and uncertainties. They are also challenged by the data sampling process which gives rise to distribution shifts when comparing past and future data.
This project is focused on research into theory and methods that can address these novel challenges and is motivated by the need to develop machine learning approaches that take into account causal relationships. The aim is to develop new ideas and methods that can tackle the uncertainties and make explicit the relevant clinical trade-offs in both predictive and prescriptive data-driven methods.
The candidate is expected to collaborate extensively with clinical experts at the other sites of the AID4BC project, as well as with clinical partners at Uppsala University Hospital, with the ultimate goal of improving patient health outcomes. The details of the project will be decided in a dialogue between the student and supervisor.
