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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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, you will work with advanced 3D in vitro models of fibrosis and metabolic disease, using multiple primary and immortalized human cell lines. Your research will focus on identifying molecular mechanisms
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) and Lundberg’s Lab at the School of Chemistry, Biotechnology and Health (CBH). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and
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to analyze human gut microbiome data from large cohorts. Responsibilities Microbe-microbe and host microbe interaction analysis in multiple cohorts (n=2500) Evaluate differential fitness of specific strains in
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diseases. Programming knowledge in R or Python is a requirement. The applicant should also have experience in machine learning. Experience in analyzing multiple MRI modalities such as sMRI, DTI and fMRI is
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/research/research-groups/rg/?rdb=g326) is involved in multiple collaborative projects funded by European Union, National Institutes for Health, and others. Together with metabolomics and exposome research
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) understand how early pathological events in AD give rise to inter-individual heterogeneity in disease expression. The project will involve integration of multiple large, longitudinal neuroimaging datasets
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multiple sub-arrays. In particular, developing methods for compensation of non-ideal system components and synchronization. Developed methods can be experimentally verified and tested on the Large
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gravimetry) to capture hydrological variables across multiple scales. In September 2025, the group launched SWIFT, a research project funded by the Middle East in the Contemporary World (MECW) programme at