23 phd-in-mathematical-modelling-population Postdoctoral positions at UNIVERSITY OF HELSINKI
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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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communities. Candidates should possess a PhD in population genetics, evolutionary ecology, or a related field, with proven expertise in spatial statistical skills. We seek candidates with the ability
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microbiomes, and antibiotic resistance in large population cohorts and big data to help mitigate the global antimicrobial resistance (AMR) crisis. AMR is one of the biggest threats to human health and is
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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
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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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associations with socio-economic markers, health, gut microbiomes, and antibiotic resistance in large population cohorts and big data to help mitigate the global antimicrobial resistance (AMR) crisis. AMR is one
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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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information, visit the lab web pages: See also our recent publication: DOI: 10.1038/s41467-024-54445-1 Your qualifications We are looking for ambitious researchers with a PhD, a solid publication record, and
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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