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and accepted to the PhD program at Stockholm University. Project description Project title: “Deep learning modeling of spatial biology data for expression profile-based drug repurposing”. A new exciting
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the molecular level. While structural predictions using deep learning methods like AlphaFold have revolutionized our understanding of sequence dependent molecular structure, we currently have much more limited
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will use machine learning methods to develop affinity ligands. These methods have been transformative for protein design, allowing generation of novel proteins which can suit a precise need. In this 4
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, structural biology, and NMR spectroscopy. The successful candidates will become a part of an international multidisciplinary environment and will receive ample opportunities for learning, collaboration and
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Are you interested in developing computational tools and learning strategies for understanding health and disease at the microscopic scale? Would you like to be part of a research team with skilled
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. Rocío Mercado Oropeza, applies machine learning to molecular engineering problems in life sciences and drug discovery, and is based in the Division for Data Science and AI within the CSE Department
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of MSI advances our understanding of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as
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Uppsala University, Department of Information Technology Are you interested in developing new image analysis and machine learning methods for improved cancer understanding, diagnostics, and
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Sciences division. This multidisciplinary team utilises a combination of machine learning and mechanistic modelling to derive models and scientific insights from data, which both support and enhance drug
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. We care about creating a positive, respectful, and stimulating environment, valuing communication and collaboration and a workplace that promotes learning and development for all. We are committed