Role: Graduate Research Assistant / Remote Sensing Data Scientist
Commitment: Part-time (approx. 16–20 hours/week), 1.11.2026 to 31.5.2027
Location: Copenhagen
Applications will be reviewed on an ongoing basis, deadline 16. October.
The challenge
Trees are at the center of some of today’s biggest questions. Are supply chains deforestation free? How can we improve regenerative forestry operations? What are the climate impacts on today’s forests? Earth observation can answer many of these questions at scale. But turning pixels into actionable datasets that people can trust is still a hard task to solve.
That’s what we are working on at CanopyMetrics. We are researchers at the TreeSense center at IGN, establishing an early-stage spin-off. The idea is to build high-resolution tree monitoring from satellite imagery, LiDAR, and other earth observation data, powered by deep learning. All of this will flow together in a platform to combat deforestation, track forest growth, or assess disturbance risk to support businesses and governments in their operations and compliance with environmental regulations. To this end we are looking for a Remote Sensing and Deep Learning specialist (student assistant or recent graduate) to develop and implement new models we can use in our prediction pipelines.
What you'll do
Data Curation: Source, clean, and manage diverse earth observation datasets and reference data (e.g., Sentinel, PlanetScope, aerial imagery, LiDAR, forest inventories) and turn it into analysis-ready training datasets.
Build, train and evaluate deep learning models in PyTorch, for example for tree detection or canopy cover.
Try out new ideas, including new sensors, foundation models and methods from recent papers, and tell us what works and what doesn't.
Be an active part of the team and have an impact on where our journey is going.
What we're looking for
You’re currently pursuing or have recently finished a Master’s in Remote Sensing, Geoinformatics, Computer Science, or similar, and you enjoy combining data, code, and solutions for the real world.
You have some of the following skills (Nobody ticks all boxes, so apply anyways and we’ll see how you fit in):
You know remote sensing and GIS: rasters/vectors, coordinate systems, and have practical experience with different data families (optical, SAR, LiDAR, ground data, etc.)
You are proficient in Python and have used the standard geospatial libraries (Rasterio, GeoPandas, Shapely, GDAL, laspy, or similar)
You are familiar with deep learning workflows, frameworks like PyTorch, and potentially even Geospatial Foundation Models.
You are curious, take ownership and enjoy working in a small team.
You have permission to work in Denmark and can be present on-site.
What we offer
Highly flexible hours that fit around your university schedule and lectures.
Work on a product where your validation directly impacts tree monitoring and environmental transparency.
Potential for continued employment in an early-stage spinoff if funding continues.
Interested? Send us your CV to
[email protected] or reach out if you have any questions.