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, L., Tonteri, T., Vanhatalo, J. (2024). Bayesian joint species distribution model selection for community-level prediction. Global Ecology and Biogeography, 33, e13827. Weigel. B., Mäkinen, J
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process. The second AI algorithm will take these PADs, alongside various solar eruption data, to predict the energy spectra of SEP events as measured by spacecraft. These predictions will be directly
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variability and the predictability of mechanistic CH4 models. We aim to fill the knowledge gap in the project “A holistic view of Methane turnover in northern Wetlands by Novel isotopic approach (MeWeN
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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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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