PhD Fellowship on actively learning flux observing systems
University of Oslo, Norway
Closing date: 28 February 2025
- Position as PhD Research Fellow in the Development of actively learning observing systems available at the Department of Geosciences, Faculty of Mathematics and Natural Sciences, University of Oslo.
- The fellowship period is 3 years.
- Starting date no later than October 1st 2025.
- A fourth year may be considered with a workload of 25 % that may consist of teaching, supervision duties, and/or research assistance. This is dependent upon the qualification of the applicant and the current needs of the department.
- No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo.
Project description and work tasks
The PhD fellow will contribute to the development of an observing system for land-atmosphere fluxes of carbon, water, and energy in arctic environments. Observations from eddy flux towers, drones carrying meteorological sensors and gas analyzers, soil sensors, and satellite imagery are fused with land-surface models such as CLM using data assimilation. The goal is to develop an adaptive experimental design framework for the observing system to guide ongoing measurement campaigns and targeted, computationally expensive, model simulations. This experimental design process is envisioned to update iteratively as new data become available to optimally infer surface fluxes across the landscape.
The work will build on and extend the existing infrastructure at the Department of Geosciences, including mobile flux towers and drone-based observing systems developed in-house.
Fieldwork for testing newly developed algorithms is anticipated in mainland Norway, Svalbard, and abroad.
Funding is also available for conference attendances and research visits with external collaborators.
The position is part of the ERC-funded project “Actively learning experimental designs in terrestrial climate science (ACTIVATE)”:
https://www.mn.uio.no/geo/english/research/projects/activate/index.html
The PhD fellow will be part of a growing team of researchers, postdocs and PhD students working on intelligent observing systems using machine learning and data assimilation methods in the ACTIVATE project.