Postdoctoral Researcher: Drone Methane Sensing & ML

Aarhus University (AU); Aarhus; On-site; DKK 420,000 - 540,000; Full time

Job summary

1000scholars invites a postdoctoral researcher to join the GHG-MONITOR project at Aarhus University. You will process and analyse hyperspectral observations to quantify methane emissions, and explore combining drone data with satellite observations using machine learning and modelling to improve monitoring.

Field campaigns will be part of the work, and you will collaborate with researchers and external partners, contributing to publications, project reporting and the delivery of the monitoring

Qualifications

• PhD in remote sensing, computer science, atmospheric or environmental science, geoscience, physics, engineering or a related discipline.
• Experience with quantitative data analysis and scientific programming (Python preferred).
• Knowledge of hyperspectral imaging, atmospheric spectroscopy, greenhouse-gas measurements, UAV observations or radiative-transfer modelling is advantageous.
• Ability to work independently, collaborate across disciplines, and communicate effectively in English.

Responsibilities

• Process and analyse hyperspectral observations to quantify methane emissions.
• Develop and validate methods for methane quantification; explore combining drone data with satellite observations using ML and modelling.
• Participate in field campaigns and collaborate with researchers and external partners.
• Contribute to publications, project reporting and delivery of the monitoring solution.

Skills

Quantitative data analysis

Scientific programming

Hyperspectral imaging

Independent work

Cross-disciplinary collaboration

English communication

Education

PhD in remote sensing or related field

Tools

Python

Job description

1000scholars invites a postdoctoral researcher to join the GHG-MONITOR project at Aarhus University. You will process and analyse hyperspectral observations to quantify methane emissions, and explore combining drone data with satellite observations using machine learning and modelling to improve monitoring.

Field campaigns will be part of the work, and you will collaborate with researchers and external partners, contributing to publications, project reporting and the delivery of the monitoring