Publisher
source

Norwegian Institute of Bioeconomy Research

PhD in Multimodal AI for Forest Biodiversity Mapping Norwegian Institute of Bioeconomy Research in Norway

Degree Level

PhD

Field of study

Computer Science

Funding

3-year PhD position remunerated according to the Norwegian State Salary Scale as PhD research fellow, position 1017, salary NOK 555,000–635,000 depending on qualifications and experience. Includes membership in the Norwegian Public Service Pension Fund with occupational pension, occupational injury and group life insurance, and low-interest home loans.

Deadline

Oct 25, 2026

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Country

Norway

University

Norwegian Institute of Bioeconomy Research

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Keywords

Computer Science
Environmental Science
Deep Learning
Biology
Remote Sensing
Earth Science
Rainforest Ecology
Self-supervised Learning
Statistics
Geospatial Information

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About this position

PhD opportunity in Multimodal AI for Forest Biodiversity Mapping at the Norwegian Institute of Bioeconomy Research (NIBIO), Norway. The project is part of the National Forest Inventory and focuses on developing cutting-edge multimodal artificial intelligence methods for forest biodiversity mapping and monitoring.

The research will combine heterogeneous data sources such as remote sensing, forest inventory data, existing forest maps, environmental and climatic variables, and large-scale AI representations. A central challenge is to learn integrated representations of forest ecosystems from data with different resolutions, coverage, and supervision levels, then use them to map old-growth forests and key biodiversity habitats at regional and national scales.

The position is a 3-year PhD located at NIBIO’s headquarters in Ås, Norway, and the candidate will be enrolled in a PhD programme at the Norwegian University of Life Sciences (NMBU). The candidate will join the emerging AI for Nature research environment jointly developed by NIBIO and NMBU.

Research themes and keywords: multimodal learning, deep learning, representation learning, self-supervised learning, foundation models, geospatial data, forest ecology, biodiversity monitoring, Earth observation, and large-scale environmental modelling.

Eligibility highlights: a relevant Master’s degree; strong machine learning/deep learning background; Python and preferably PyTorch; experience with large or complex datasets; fluent English. The post also notes that applicants without Norwegian/Swedish/Danish at A2 level upon employment will receive free Norwegian language training.

Funding: salaried PhD research fellow position with a Norwegian State Salary Scale range of NOK 555,000–635,000, plus pension and insurance benefits.

Deadline: 25 October 2026. Apply electronically through the Jobbnorge link and include your CV; bring diplomas and recommendation letters if invited to interview.

Funding details

3-year PhD position remunerated according to the Norwegian State Salary Scale as PhD research fellow, position 1017, salary NOK 555,000–635,000 depending on qualifications and experience. Includes membership in the Norwegian Public Service Pension Fund with occupational pension, occupational injury and group life insurance, and low-interest home loans.

What's required

A Master’s degree in machine learning, artificial intelligence, computer science, remote sensing, geomatics, data science, or a forest/environmental science discipline with a strong quantitative or AI component is required. Applicants should have strong knowledge of machine learning and deep learning, solid programming skills preferably in Python and modern deep-learning frameworks such as PyTorch, experience with large or complex datasets, and fluent English speaking and writing skills. The successful candidate must meet the admission requirements for the relevant PhD programme at the Norwegian University of Life Sciences. Applicants without Norwegian, Swedish, or Danish at level A2 upon employment will receive free Norwegian language training.

How to apply

Apply electronically via the link on the page and submit your CV. If invited to interview, bring originals of diplomas and letters of recommendation and upload copies with the electronic application/CV.

More information can be found here

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