19 phd-in-mathematical-modelling-population Postdoctoral positions at UNIVERSITY OF HELSINKI in Finland
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of the near critical Ising model, or the massive Thirring model. Qualifications We are looking for applicants with a PhD in mathematics or theoretical physics, with experience in mathematically rigorous
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and population genetics is required. You should be acquainted or capable of learning the modelling associated with genome-wide association studies (GWAS), landscape genomic analyses, and population
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the benchmarking of continuum modeling code together with GTK. Take part in dissemination, outreach, and teaching activities. Profile of the candidate PhD in relevant field (Physics, Chemistry, Geology, Fluid
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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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(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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metagenomics assembly” funded by the Research Council of Finland in the research group of University Lecturer Leena Salmela. We develop models, algorithms and data structures for high throughput sequencing data
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using unique novel mouse models, spatial technologies and analytical methods. Postdoctoral Researcher in Functional Cancer Microbiome through the NORPOD program NORPOD is a collaborative postdoctoral
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facilities with access to the state-of-the art technologies. Qualifications We invite applications from candidates with a solid theoretical and wet-lab background and a PhD degree in cell/molecular biology
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to contribute to the preparation of funding applications, supervise/mentor MSc and PhD students and contribute to teaching activities. Requirements We look for candidates in the field of sustainability assessment
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Cancer Medicine at the Faculty of Medicine, University of Helsinki. The project will focus on developing approaches for gene regulatory network modeling on deconvoluted bulk data, with applications to pan