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developed will be based on pseudonymization, anonymization, and synthetic data generation. Using real health data as a source of information, we aim to create test datasets and statistical models
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the entire population. The project utilises advanced statistical methods such as multilevel models (mixed models), fixed-effects models, cluster analysis, and sequence analysis. The selected researcher is
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, programming, Linux, data, and infrastructure perspective: short-term projects helping researchers with specific tasks, so that the researchers gain competence to work independently. Provide good role models
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mental health and computational social science, using large-scale social media analysis, smartphone-based sensing, and agent-based modeling. Combining macro-level patterns with micro-level behavioral data
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(FIMM) , University of Helsinki, is currently seeking a highly-motivated postdoctoral researcher to join our interdisciplinary team. Project overview This project aims to develop machine learning models
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methodology for analysing long-term spatially structured data sets within a joint species distribution modelling framework. For more information on REC, please see https://www2.helsinki.fi/en/researchgroups
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distribution modelling framework. For more information on REC, please see https://www2.helsinki.fi/en/researchgroups/research-centre-for-ecological-change/ A POSTDOCTORAL RESEARCHER POSITION IN BIODIVERSITY
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manipulation of developing mouse organs. The project will focus on how signaling pathways operate at the intersection of growth control and branching morphogenesis in the developing mammary gland and will use
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immune-system related diseases such as immunodeficiency and cancer. We use a wide range of techniques such as mouse models, tumor models, in vivo immune cell migration and other functional assays, flow
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tools, including 4D point cloud modeling and state-of-the-art machine learning and deep learning techniques (such as generative adversarial networks), with empirical fieldwork in Norwegian glacier