Research Scientist, Doctoral Researcher, soil carbon modeling

Natural Resources Institute Finland / Luonnonvarakeskus; Helsinki; On-site; EUR 42,000 - 54,000; Full time

Job summary

Natural Resources Institute Finland (Luke) in Helsinki invites a highly motivated doctoral researcher for the DECODE project to study how temperature sensitivity of soil organic carbon decomposition varies with organic matter quality and microbial community composition.

The role centers on Bayesian data assimilation within the Q-model framework using large European datasets and aims to improve climate models.

Qualifications

• Higher university degree in a relevant field (e.g. applied mathematics, statistics, physics, computational science, environmental science, agriculture, ecology, microbiology).
• Strong mathematical skills and ability to apply them to solve ecological problems through modeling.
• Experience with Bayesian inference, MCMC methods, high performance computing or spatial modelling is an asset.
• Good programming skills (R, Python, or others) and ability to read, write and debug code independently.

Responsibilities

• Develop a Bayesian data assimilation framework built around the Q-model using large datasets.
• Integrate diverse data sources and perform model development.
• Utilize CSC’s supercomputing infrastructure and publish results in peer‑reviewed journals.
• Collaborate with international partners and complete doctoral studies through the University of Tampere.

Education

Higher university degree in a relevant field

Tools

R

Python

Job description

Do you have good mathematical skills and interest in ecological modeling? We are looking for a motivated doctoral researcher for the Academy of Finland’s 4-year DECODE project, modeling ecological interactions in soil organic carbon dynamics.

The doctoral candidate will contribute to the Academy of Finland funded DECODE project (Decoding ecosystem carbon and organic matter dynamics as an ecosystem property), investigating how the temperature sensitivity of soil organic carbon decomposition varies with organic matter quality and microbial community composition in European soils. The results will improve climate models by enabling more accurate estimates of future soil CO₂ emissions and associated feedback mechanisms.

The candidate will develop a Bayesian data assimilation framework built around the Q-model using large datasets (e.g. Holisoils, LUCAS, ICP Forests) and scaling the results up to European level. The tasks involve integrating diverse data sources, model development, using CSC’s supercomputing infrastructure, and publishing results in peer-reviewed scientific journals under the supervision of the project’s principal investigator.

The position is located in Luke in Helsinki within an international research team, while doctoral studies will be completed through the University of Tampere. The project includes collaboration with several European research organizations and a research visit to a partner institution, the University of Oslo. The position offers an opportunity to conduct multidisciplinary, cutting-edge research at the intersection of terrestrial ecology and mathematics, providing strong career prospects in academia, climate risk assessment and industry.

The position will start on 1.12.2026, or as agreed.

Our expectations of the applicant

We are looking for a highly motivated doctoral candidate with good mathematical skills, a curious mind, and the ability to apply the expertise to solving challenging ecological problems through ecological modeling. Applicants must hold a higher university degree in a

relevant field (e.g. applied mathematics, statistics, physics, computational science, environmental science, agriculture, ecology, microbiology). Applicants are expected to have an interest and willingness to pursue an article-based doctoral dissertation.

The applicant should have good mathematical skills and be capable of working with linear and partial differential equation systems. Experience with Bayesian inference, MCMC methods, high performance computing or spatial modelling is seen as an asset. The candidate should have good programming skills (e.g., R, Python, or others) and be able to read, write, and debug code independently. Knowledge of soil science is not required, but a degree in a relevant field or otherwise demonstrated strong expertise in soil science, combined with the required mathematical skills, is considered an asset.

Good communication skills and the ability to work as a part of a multidisciplinary international research team as well as independently is required. A successful candidate has good spoken and written skills in English. Previous experience with contributing to scientific publications can be seen as an asset.