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(Spatial VDJ). Using established and newly developed algorithms, we map B cell evolution within tissues, including class switching and somatic hypermutation, and identify putative candidate tumor-regulatory
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of approaching reconstruction and variability analysis. The project combines applied mathematics, computational imaging, and structural biology. You will develop algorithms, implement and test software tools, and
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epidemiology and biology of infection, which is a fully funded, four-year PhD student position. Data-driven life science Research School Data-driven life science (DDLS) uses data, computational methods, and
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to receiving your application! 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
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the national Data-Driven Life Science (DDLS) program. About the position and the project As an industrial PhD student, you will be employed by the startup company PredictMe AB while being formally enrolled as a
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Collaboration Conduct research together with academic, industrial, and healthcare partners in a highly collaborative environment. Who we are looking for A highly motivated PhD candidate excited by computational
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-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 processes to human
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programme. The doctoral programme comprises 25 credits and is offered in two study variants: 25 credits spread over 8 terms (a total of 4 years) or over 12 terms (a total of 6 years), starting each autumn and
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effects. Importantly, computational predictions will be directly linked to experimental evaluation of GalNAc-conjugated RNA therapeutics. This position offers a unique opportunity to work in a highly
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of polyploid genome evolution across contrasting timescales. The student will receive interdisciplinary training in bioinformatics, evolutionary genomics, and high-performance computing within the DDLS data