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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 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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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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Are you passionate about applying computational approaches to solve problems within chemistry and biology? We are now looking for an Industrial PhD student in Data-Driven Life Sciences to work on a
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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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study plan. For a doctoral degree, the equivalent of four years of full-time doctoral education is required. The research group Our lab is advancing precision medicine through deep learning models
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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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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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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