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project: Computational methods for complex SV detection using sequencing data Main supervisor: Kristoffer Sahlin, ksahlin@math.su.se . Co-supervisor: Adam Ameur, adam.ameur@igp.uu.se . In the Department
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year project, funded by the DDLS program, we aim to develop AI-based tools in design of affinity ligands, such as the prediction of binding interactions between proteins. Data-driven life science (DDLS
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and computational modeling to understand complex biological processes. Experience in statistical modeling, machine learning, or analysis of spatial or high-dimensional biological data is considered
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to study host-microbiome interactions at the spatial level in the colon. The research activities of the doctoral student will focus on the experimental and computational analysis of spatial gene expression
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KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science Project description Third-cycle subject: Computer Science The project focuses on advancing species
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life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health
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KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science Project description Third-cycle subject: Computer Science This project involves generative modeling
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) programme and research school Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures
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data-driven infection research, such as the development or innovative application of computational tools to analyze and integrate data or mathematical models to understand complex systems. Experience
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models of interacting ecological and evolutionary (eco-evolutionary) processes. You will conceptualize and model the complexity in which ecological divergence and speciation drive an adaptive radiation