Summer Student : Geospatial Deep Learning for Forestry Applications - Scion

Overview

Summer Student : Geospatial Deep Learning for Forestry Applications – Based in Rotorua. Approximate dates : 1 December 2025 - 20 February 2026.

As part of the Government's reset of the science, innovation and technology system, on 1 July 2025, AgResearch, Manaaki Whenua - Landcare Research, Plant & Food Research, and Scion merged to form the New Zealand Institute for Bioeconomy Science, trading as the Bioeconomy Science Institute. This merger has created a world-class research organisation of globally significant size and scale, with over 2,000 employees — including scientists, researchers, and support staff. It brings together fundamental science knowledge and expertise in the natural resources that underpin the bioeconomy and the native estate, alongside applied research capabilities in manufacturing, agritech and biotech, and the food and fibre sectors. Our people drive innovation and commercial outcomes in the bioeconomy, using research and technology to support enduring economic growth and resilience, a healthy environment, and positive social outcomes for Aotearoa New Zealand.

This position sits under the current Scion group of the Bioeconomy Science Institute. Scion specialises in research, science and technology development for the forestry industry, wood products and wood-derived materials. We lead new technology development for renewables, bioproducts and energy and the establishment of a broader-based circular bioeconomy. Scion hosts a number of summer students, each with differing specialities, from tertiary institutes across New Zealand at both our Rotorua and Christchurch Campus. The programme involves a student completing a 12-week, full-time, research project under the direction of Scion supervisors (allowing time for holidays over the Christmas and New Year). Students are able to gain hands on, paid, work experience in the field of which they are studying, where they get to work shoulder to shoulder with Scion's internationally recognised staff.

We are seeking a motivated student to work on the Digital Twin for Planted Forest and CHM depth estimation projects.

What the Student Will Do

• Extract geospatial and remote sensing data from various sources under supervision
• Interpret aerial, and LiDAR imagery to support data preparation and validation tasks
• Create high-quality labelled datasets for training and evaluating deep learning models
• Work under supervision to assist with development of machine learning models
• Inspect and provide feedback on model predictions

This project will expose the student to a broad range of tasks in GIS, remote sensing, and environmental data science, while also deepening their understanding of forestry applications. They will have the opportunity to develop or enhance practical skills in geospatial analysis, programming, and cloud-based data processing, as well as gain hands-on experience with cutting-edge AI tools and methods.

Qualifications, Skills And Attributes Needed

Applications close - 12 September 2025

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