Postdoctoral Research Fellow in Active Learning for Arctic observing systems (ref 303807)
Role Overview:
We invite applications for a postdoctoral position focused on developing actively learning observing systems for carbon, water, and energy exchange in Arctic environments. The position centers on creating frameworks that allow environmental observing systems to adapt and learn from data—identifying which measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field observations—including ground- and drone-based measurements—along with satellite data, into land-surface and boundary-layer models, drawing on the department’s unique infrastructure of eddy flux towers, drone platforms, and data assimilation frameworks. Field campaigns are planned in mainland Norway, Svalbard, and international Arctic sites. Funding is also available for conference attendances and research visits with external collaborators. The position is part of the ERC Starting Grant “Actively learning experimental designs in terrestrial climate science (ACTIVATE)”.
Responsibilities:
Develop actively learning observing systems for carbon, water, and energy exchange in Arctic environments. Create frameworks that allow environmental observing systems to adapt and learn from data. Integrate field observations, including ground- and drone-based measurements, along with satellite data, into land-surface and boundary-layer models. Participate in field campaigns in mainland Norway, Svalbard, and international Arctic sites. Engage in conference attendances and research visits with external collaborators.
Requirements:
Research Field: Geosciences
Education Level: PhD or equivalent
Qualifications:
Expertise in developing actively learning observing systems for environmental data. Proficiency in Bayesian inference, probabilistic modeling, and machine learning. Experience with integrating field observations and satellite data into land-surface and boundary-layer models. Familiarity with eddy flux towers, drone platforms, and data assimilation frameworks. Ability to conduct field campaigns in Arctic environments. Strong communication and collaboration skills for working with a multidisciplinary team.
What They Offer:
A postdoctoral position within the ERC Starting Grant “Actively learning experimental designs in terrestrial climate science (ACTIVATE)”. Access to unique infrastructure including eddy flux towers, drone platforms, and data assimilation frameworks. Funding for conference attendances and research visits with external collaborators. Opportunity to work with a growing team of researchers, postdocs, and PhD students on intelligent observing systems using machine learning and data assimilation methods. Full-time, temporary contract with 37.5 hours per week. Work location in Norway, specifically at the Department of Geosciences, University of Oslo.