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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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, 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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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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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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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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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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on health and prevention of non-communicable diseases. Our main focus is on gut physiology, metabolism and microbiota. Our methodological approaches include cell cultures, animal models and randomised
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Researcher will strengthen the group’s work on developing a transnational comparative model. A potential focus of their research either on the so-called ‘second-world’ literatures or the European literary
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computing in large-scale omics data analysis. Your work will focus on method development and their application to biomedical research questions. Key responsibilities include: analyzing and modeling large
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. Integrate environmental, spatial, and social data into digital twin models for scenario testing and policy simulation. Adapt co-design methods to local contexts in demonstrator sites (Portugal, Sweden, Italy