PhD Research Fellow in Active Learning for Arctic observing systems (ref 303824)

Offer Description

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 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 N...