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University of Jyväskylä

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Doctoral Researcher in Disease Ecology (PhD) University of Jyväskylä in Finland

Degree Level

PhD

Field of study

Computer Science

Funding

Fixed-term doctoral researcher position for 42 months, funded by the Research Council of Finland through Yingying Wang’s Academy Research Fellowship project. Starting salary is approximately EUR 2500 per month gross, with salary progression based on thesis progress under the Finnish University Salary System; a holiday bonus is included.

Deadline

Nov 30, 2026

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Country

Finland

University

University of Jyväskylä

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Keywords

Computer Science
Ecology
Environmental Science
Biostatistics
Biology
Mathematics
Forestry
Salud Pública
Urbanism
Disease Ecology
One Health
Statistics

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About this position

Doctoral Researcher opening in Disease Ecology at the University of Jyväskylä, Department of Biological and Environmental Science, Finland.

The project uses a One Health framework to study how urbanisation and forest management shape wildlife–pathogen–human interactions and zoonotic disease risk across Finland’s urban, rural and forest environments.

The doctoral researcher will work on quantitative ecology and large, heterogeneous datasets, including wildlife monitoring, small-mammal trapping, camera-trap observations, pathogen-screening results, land-use data and climate information. Methods include R for data processing and statistical analysis, spatial and spatiotemporal modelling, ecological community analysis, and possibly mathematical or mechanistic disease transmission modelling.

This is a PhD-level fixed-term position for 42 months, starting 4 January 2027 or as agreed, but no later than 1 March 2027. The position is funded by the Research Council of Finland through Yingying Wang’s Academy Research Fellowship project.

Eligibility highlights: a suitable master’s degree in epidemiology, biostatistics, quantitative ecology or a related field; demonstrated R and statistical modelling experience; strong interest in disease ecology, wildlife-pathogen interactions, environmental change and One Health; good English; and the ability to work independently and collaboratively. Experience with GIS, hierarchical/mixed-effects models, ecological community modelling, large datasets, or mechanistic infectious disease modelling is an advantage.

Applicants must also hold or apply for doctoral study rights at the University of Jyväskylä through the Faculty of Mathematics and Science’s separate admission procedure.

Deadline: 30 November 2026 at 23:59 (Helsinki time). Apply via the university recruitment system and upload a CV, a motivation letter, and degree documents/transcript as applicable.

Funding details

Fixed-term doctoral researcher position for 42 months, funded by the Research Council of Finland through Yingying Wang’s Academy Research Fellowship project. Starting salary is approximately EUR 2500 per month gross, with salary progression based on thesis progress under the Finnish University Salary System; a holiday bonus is included.

What's required

A suitable master’s degree in epidemiology, biostatistics, quantitative ecology or a related discipline is required, and the degree must be completed before employment begins. Applicants should have demonstrated experience using R for data processing, statistical analysis and visualisation, a sound understanding of statistical modelling, and a strong interest in disease ecology, wildlife-pathogen interactions, environmental change and One Health. Good written and spoken English is required. Experience with spatial or spatiotemporal analysis and GIS, hierarchical or mixed-effects models, ecological community modelling, integration of large datasets, or mathematical/mechanistic infectious disease modelling is an advantage. The selected candidate must also hold or apply for doctoral study rights at the University of Jyväskylä through the separate admission procedure.

How to apply

Submit the application in English through the University of Jyväskylä recruitment system. Attach a CV, a motivation letter (max two pages) describing your fit and prior R/statistical modelling work, and your master’s degree certificate or current transcript plus expected graduation date if not yet completed. Shortlisted candidates will be interviewed.

More information can be found here

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