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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from
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, computational modelling, bioinformatic analysis, and experimental vascular biology. Based in a dynamic translational research environment of data-driven life science, computational imaging, and vascular surgery
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informatics. This, to serve the biological goal of mapping out the breast cancer tumor microenvironment, understanding the regulatory signaling network, and identifying early stage progression markers and
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Sciences (SLU) in Uppsala, Sweden. Research in the Evolving Networks lab centers around understanding how species interactions change over time and space, with a focus on caterpillar-plant interactions and
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program (Data-Driven Life Science) with focus on precision medicine. Access to top-level infrastructure, a new therapy development initiative for brain diseases (CNSx3), and a strong network spanning
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relies on close collaboration with researchers at the Department of Immunology, Genetics and Pathology (IGP) at Uppsala University. The PhD position is within the Data-driven life science (DDLS) Research
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the past ten years thanks to artificial intelligence, mainly in the form of deep convolutional neural networks. In parallel, functional analysis of tissue samples via novel microscopy techniques and spatial
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of cancer cells. The models are trained on high-throughput datasets, including metabolomics, proteomics, and transcriptomics, and constrained to align with the cell’s molecular networks. This allows us to