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approaches of genetic epidemiology, molecular epidemiology, pharmaco-epidemiology, and machine learning using large-scale population-based cohorts, national registries, electronic healthcare records, and multi
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the AI-Tomo project funded by the Swedish Research funding agency, Vinnova. The project has been established to support and develop the exploitation of machine learning tools to accelerate the analysis
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research subject area aims to lead to innovative development and/or application of novel data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. Lund
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hematopoietic cells in general and erythroid cells in particular. Experience working with large genomic datasets. Knowledge of statistics and machine learning applied to biological data. Experience working in a
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. Furthermore, the findings will be followed up with various bioinformatics methods. You will also work with machine learning to develop new methods for classifying diabetes and predicting the risk of
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relevant subject Additional requirements: at least one course in Programming and one course in Optimization, at least one 2nd cycle course in Stochastic processes, Machine learning, or related subjects
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deconvolution and machine learning methods for prognosis and therapeutic biomarker development. The collaborative research may include but is not limited to software tool dissemination, biology discovery, and
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of teaching on campus and remote learning online. The teaching mainly encompasses basic courses and to some extent advanced courses in geographic information science (GIS) and within the department's other