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of the workplace The Genetic and Molecular Epidemiology Unit at the Department of Clinical Sciences conducts research primarily on data-driven solutions in precision medicine, with focus on precision prediction
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. David Marlevi, Prof. Ulf Hedin, and Dr. Ljubica Matic to improve stroke risk prediction for patients with carotid atherosclerosis using a multidisciplinary combination of data-driven imaging
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life science. The aim of this PhD position is to develop a novel phylogenetic approach to predict unknown species interactions. For that, the student will compile all available data on host use
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evaluating designed backbones and predicting the functional effects of protein variants. In addition, the doctoral student will be part of the DDLS initiative, and participate in the DDLS Research School
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focuses on predicting cell type-specific responses to genetic alterations, identifying transcriptional signatures indicative of treatment sensitivity, and predicting the effects of the cell type composition
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components in time and space, from single molecules to native tissue environments. The project The industrial PhD student will develop AI and machine learning models to predict drug metabolism, a critical area
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immunity and develop diagnostic approaches that accurately predict therapy benefit and enable successful individualized cancer therapy planning. Contemporary AI-based approaches show great promise to advance
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clinical prediction of progression remains difficult, leading to over- and undertreatment of women with particularly early breast cancer. Over the past decade, spatial tissue analysis techniques have been
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prediction or patient stratification based on, among other things, molecular data. More information about the group’s activities can be found at: https://www.uu.se/en/department/immunology-genetics-and
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or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale experimental data are now available or it may use population-scale genetic, clinical, or public health