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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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, are available and encouraged. Candidates should have a doctoral degree in ecological statistics, statistics, or similar area, and previous experience in working with hierarchical Bayesian models and Markov chain
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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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, Finland [map ] Subject Area: Mathematical Physics Starting Date: 2025/10/01 Salary Range: 3800-4050 euros/month Appl Deadline: 2025/08/15 11:59PM (posted 2025/07/10, listed until 2025/08/15) Position
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, the appointee’s work will cover some of the following areas: Development of an isotope version of the process-based CH4 model and parameter optimization for different wetland types. Coupling the updated CH4 model
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parental leave usage across different population groups and how the use of leave has changed over time and in various social environments: workplaces, residential areas, and extended family networks
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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
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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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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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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