PhD Fellow: Multimodal AI for Forest Biodiversity Mapping

1000scholars; Ås; On-site; NOK 450,000 - 550,000; Full time

Job summary

The National Forest Inventory invites applications for a PhD candidate to develop cutting-edge multimodal AI methods for forest biodiversity mapping. The project will integrate remote sensing, forest inventory data, existing maps and climatic data to create unified representations across scales for biodiversity knowledge gaps.

The PhD is a 3-year position based at Ås, with joint supervision from NIBIO and NMBU, enrolling at NMBU.

Qualifications

• Must pursue a PhD in a relevant field and conduct cutting-edge AI research.
• Experience with interdisciplinary datasets and geospatial data is desirable.
• Strong ability to publish results in scientific venues.

Responsibilities

• Develop multimodal deep learning methods to integrate heterogeneous forest datasets.
• Investigate learning across different spatial resolutions and modalities.
• Explore foundation-model and self-supervised learning for forest and biodiversity data.
• Publish findings in international journals and conferences.

Skills

Multimodal learning

Representation learning

Self-supervised learning

Foundation models

Education

PhD candidate in relevant field

Job description

The National Forest Inventory invites applications for a PhD candidate to develop cutting-edge multimodal AI methods for forest biodiversity mapping. The project will integrate remote sensing, forest inventory data, existing maps and climatic data to create unified representations across scales for biodiversity knowledge gaps.

The PhD is a 3-year position based at Ås, with joint supervision from NIBIO and NMBU, enrolling at NMBU.