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hold a PhD degree in ecology, biology, aquatic ecology, environmental sciences, biogeography, or a closely related field by the start of the appointment. The successful candidate is expected to have: A
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/careers ). YOUR PROFILE PhD in biology, mathematics, or a related field Strong background in mathematical or computational modelling Ability to develop and pursue independent research questions Interest in
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development (https://www.helsinki.fi/en/about-us/careers ). Required qualifications PhD (or near completion) in evolutionary biology, ecology, genetics, or related fields. Expertise in molecular genetics
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application: cover letter, describing the previous research experience and expectations from the future work. CV (including ORCID iD and publication list). PhD diploma. contact details of at least two
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://www.helsinki.fi/en/about-us/careers/why-university-helsinki How to apply Please submit your application in English including: • cover letter • CV • PhD degree diploma. Applications should be submitted via
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pension fund, a generous holiday package, sports facilities, and opportunities for professional development (https://www.helsinki.fi/en/about-us/careers ). Required qualifications PhD (or near completion
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. The candidate should hold PhD degree in microbiology, bioinformatics or related field, and good written and verbal communication skills in English are necessary. The postdoctoral position is for 4 years starting
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including: • cover letter • CV • PhD degree diploma. Applications should be submitted via the University of Helsinki recruitment system using the “Apply Now” link no later than 1.1.2026. Early applications
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(linking phenotypes, imaging, cytometry, or other readouts to transcriptomics) Statistics / machine learning for biological inference (model validation, differential state testing, embeddings/classifiers
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-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning