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. Project description Data-driven mathematical and statistical models are increasingly used in life science research and healthcare. Quantifying the uncertainty associated with these models is crucial
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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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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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research environments in Computational Science, the research and education has a unique breadth, with large activities in areas such as numerical analysis, mathematical modelling, development and analysis
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and variation. The successful candidate will develop innovative methods and models to advance our understanding of genome evolution and variation. The position is based in the Computational Genomics
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to develop and utilize innovative, interpretable data-driven analysis methods to significantly advance our understanding of immune cell inter-relations within the cancer microenvironment. We will apply
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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
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data-driven diagnostics. About the project Cancer treatment often involves surgery, chemotherapy, and radiotherapy. While these treatments have improved outcomes, they can be long-lasting and cause
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of pancreatic cancer. This group is led by Associate Senior Lecturer Dr. Qiaoli Wang, as part of the SciLifeLab & Wallenberg National Program for Data-Driven Life Science (DDLS) . Group members are enrolled in
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through the department’s involvement in engineering and master’s programs. Our research and teaching are conducted within seven divisions with different research focus. Read more about us here About the