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Postdoctoral – Remote Sensing & Fuel Moisture Modelling - Scion

ScionWorkFromHome, Canterbury, New Zealand

15 hours ago

Job description

Overview

Location : Christchurch or Rotorua (Remote). Duration : 2 years. Organisation : Scion Research.

Are you passionate about using satellite data to solve real-world environmental challenges? Join a cutting-edge international collaboration aimed at transforming wildfire preparedness in New Zealand and beyond.

We are seeking a highly motivated Postdoctoral Researcher to lead the development of near real-time predictive models of vegetation fuel moisture using a combination of field data and satellite imagery from Sentinel-2 and Landsat (HLS). This role is part of a multi-institutional and cross-country initiative led by Scion, in partnership with NASA Ames, the US Forest Service, Australian National University, and Fire and Emergency NZ.

The successful candidate will play a central role in the development of operational tools that provide updated fuel moisture products every 3-5 days and map fuel type quarterly to biannually—filling a critical gap in fire danger prediction. Your work will directly contribute to improving New Zealand's ability to anticipate and respond to wildfire and drought risks, protecting communities, ecosystems, and the economy.

Key Responsibilities

Acquire, process, manage, and integrate field and satellite data (Sentinel-2, Landsat / HLS).
Develop, calibrate, and validate predictive models of vegetation fuel moisture.
Integrate auxiliary datasets (e.g., weather, topography, vegetation type) into modelling workflows.
Collaborate with national and international partners to align methodologies and share insights.
Lead authorship of 1-2 peer-reviewed publications.
Contribute to the development of long-term model maintenance and update strategies.

Required Qualifications and Experience

PhD in Remote Sensing, Environmental Science, Geospatial Science, or a related field.

Experience processing satellite imagery (Sentinel-2, Landsat / HLS), including atmospheric correction, cloud masking, and geometric / radiometric preprocessing.

Excellent proficiency in spatial and statistical programming (e.g., R, Python, or Google Earth Engine).

Proficiency in, and commitment to, version control (e.g., Git), reproducible research practices, and documentation.

Strong understanding of vegetation optical properties, including vegetation indices and their use in environmental modelling.

Demonstrated experience in developing and validating empirical or statistical models using remote sensing and environmental datasets. Excellent written and verbal communication skills.

Preferred Qualifications and Experience

Experience with cloud-based geospatial platforms (e.g. Google Earth Engine) for time series or large-scale analysis.

Familiarity with ecological disturbance, vegetation stress, or fire science concepts.

Experience working with large datasets and automating geospatial workflows.

Proficiency with GIS tools (e.g., QGIS, ArcGIS) for spatial analysis and visualization.

Experience developing statistical or machine learning models to estimate fuel moisture or similar environmental variables.

Experience integrating field measurements with remote sensing data in predictive models.

Prior publication record in remote sensing or environmental modelling.

What We Offer

Opportunity to work on a high-impact, internationally collaborative project.

Supportive research environment with access to cutting-edge data and tools.

Flexible working arrangements.

Mentorship and professional development opportunities.

Applicants must be eligible to work in NZ - be a NZ / Australian resident or hold a valid NZ work permit

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Remote • WorkFromHome, Canterbury, New Zealand