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strives to be a workplace free from discrimination and with equal opportunities for all. Generous financial support for conducting research and recruiting staff, such as postdocs and PhD students, is linked
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) training personalized computational models in new contexts, and (iii) studying in-silico clinical intervention strategies. The postdoctoral fellow will have the opportunity to: Learn about computational
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data at an internationally competitive level. Experience of biostatistics or machine learning approaches Proficiency in a scripting language like R or Python, as well as ability to work efficiently in a
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of the infrastructure. We envision that you will start with the easy assignments and then, as you learn and become more experienced, progress to increasingly difficult/qualified work. Qualifications The requirements
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grow into real impact. At the division of Data Science and AI , we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
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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 flow matching. Therefore, the doctoral
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to integrate research in the relevant subject area, subject didactic, and higher education into teaching is also required. documented ability to teach in Swedish or English. In addition to academic
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++ or similar) and an interest in quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not
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biology and genetics. Experience using Git for version control. Ability to quickly learn new skills. Experience working in a Unix/Linux environment. Excellent verbal and written English skills. We