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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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. The candidate will combine experimental and computational approaches. The project will start with bioinformatics-driven analysis, followed by integration of data generated from experimental models. Over time, the
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have a background in bioinformatics, computational cancer biology, or related fields with experience in cancer and data-driven research. For more details about our research visit: www.alundberg.org
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KTH Royal Institute of Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health Project description Third-cycle subject: Medical Technology (Joint KTH-KI program) In
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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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and experiences. We regard gender equality and diversity as a strength and an asset. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) is a 12-yr initiative funded with
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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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) 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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, within the Centre for Image Analysis at the Department of IT and conducted alongside researchers developing computational methods with a particular focus on deep learning and image analysis. The project
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diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study