PhD Research Fellow in Active Learning for Arctic observing systems (ref 303824)
Core
Develop an adaptive experimental design framework for an observing system measuring land-atmosphere fluxes of carbon, water, and energy in arctic environments using data assimilation.
Role type
PhD Research Fellow in Active Learning and Data Assimilation
Builds
Adaptive experimental design framework for terrestrial climate science observing systems
Domain
Geosciences / Arctic Climate Science / Machine Learning
Deliverable
research
Required skills
data assimilation, machine learning, experimental design, land-surface modeling, algorithm development
Preferred skills
fieldwork in arctic environments, drone operation, satellite imagery analysis
Technologies
eddy flux towers, drones, gas analyzers, soil sensors, satellite imagery
Responsibilities
Fuse observations from various sources with land-surface models; Guide ongoing measurement campaigns and targeted model simulations; Test newly developed algorithms through fieldwork; Participate in conference attendances and research visits.
Seniority
PhD Candidate