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. The project explores the role of tumor-promoting inflammation in cancer progression through bioinformatics-driven, machine-learning and multi-omics analyses integrated with experimental data. Ideal candidates
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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 and statistical methods, with demonstrated experience in reproducible and scalable bioinformatics environments. The applicant will work in close collaboration with other Engblom lab team members who
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) strategies, primarily revolving around interpretable ML and generative AI, to study complex biological processes. This project combines timely analytical challenges with deep rooted applications in life
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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 and cellular
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pipeline and implement it in a high-performance computing environment. Qualifications Requirements: A Master’s degree (or equivalent) in bioinformatics, biostatistics, computational biology, data science
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part of an ERC starting grant and involves studying the impact of disease on endosomal properties and the processing of lipid nanoparticles. Key techniques will include omics, cell culture and small
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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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School. 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 and
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Sintorn, Professor in digital image processing, at the Department of Information Technology and conducted alongside researchers developing computational methods with a particular focus on deep learning and