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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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Doctoral Researcher in statistical signal processing. The Structured and Stochastic Modeling Group, headed by Prof. Filip Elvander, conducts research in statistical signal processing, ranging from
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of Advanced Mathematics for Sensing, Imaging and Modelling (2024–2031). The group is also supported by a Research Council of Finland Fellowship under the project SPARSe (2025–2029), further strengthening its
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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
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, metabolism and microbiota. Our methodological approaches include cell cultures, animal models and randomised controlled trials. Responsibilities and qualifications We are looking for a motivated postdoctoral
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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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models using sophisticate genetic tools, in vivo time-lapse imaging and multi-omics methods to decipher the underpinning mechanisms of regeneration. Our findings provide new targetable mechanisms
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stimulation) to be used in animal model experiments. The work is based on previous development on materials, devices and cell experiments within this project and it is supported by the whole multidisciplinary
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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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by integrating computational mental health and computational social science, using large-scale social media analysis, smartphone-based sensing, and agent-based modeling. Combining macro-level patterns