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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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interdisciplinary project. The project concerns algorithm design, implementations of algorithms, and simulated and biological data analysis. The student is expected to learn a bit of relevant molecular biology to
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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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an individual study plan. For a doctoral degree, the equivalent of four years of full-time doctoral education is required. The research group The position is based in Joakim Dahlin’s team at Karolinska Institutet
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collaborations. The research group The position is in Ben Murrell’s group in MTC, based in the Biomedicum, in Karolinska’s Solna campus. The lab has worked across the experimental/computational interdisciplinary
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such as: Good analytic skills, good collaboration and communication skills. Ability to work independently and to drive projects based on personal initiatives. Good skills in organization and planning work
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responsibilities will include investigating the structural and biophysical properties of miniaturized tumor environment models. You will be responsible for fabricating these systems using both cleanroom-based and
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candidates will be based on their capacity to successfully complete the program. Important criteria when assessing this capacity are; documented knowledge and skill in the field of the thesis project, written
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dynamics of plasmid copy number in pathogenic bacteria, with a strong focus on infection biology and evolution of antibiotic resistance. The goals for this project are i) to better understand the role
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staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition. Lund University welcomes applicants with diverse backgrounds