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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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at the intersection of computational biophysics, AI/ML modelling, and biophysics/chemistry-driven approaches to understanding and developing therapeutic lipid nanoparticles. Focus areas of the position Chemistry
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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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, 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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and motivated PhD student to join an interdisciplinary project that combines computational biology, spatial transcriptomics, and tumor modeling to understand how the aggressive brain tumor glioblastoma
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vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid
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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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for computational protein design, but also for an array of other applications related to modeling and understanding proteins and their behavior. The heart of the project will be the development and refinement
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networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid academic background with thorough computational and
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methods in applied mathematics and computational modeling, this specific project aims to uncover new insights into how blood cells form in both healthy and disease states. A key objective is to model