PD – Postdoctoral Position opened to candidates of any nationality to work on leaf temporal dynamics and extreme events across tropical seasonal biomas in Brazil.
We are hiring a Postdoctoral Researcher to study structure and function of trees using drone-based remote sensing in the Genome Canada funded FastPheno project.
PhD fellowship – global meta-analysis and modelling of terrestrial ecosystem NPP – focus on the environmental and anthropocentric drivers of NPP
A fully funded 4-year PhD position is open at the Catchment and Wetland Sciences (CAWS) Research Group (www.caws.ualberta.ca), in the Department of Renewable Resources at University of Alberta, under the supervision of Dr. David Olefeldt. Start of program is January 2023, or May 2023. We are looking for a talented student with interests in ecosystem greenhouse gas balance, soil biogeochemistry, peatland ecology, and Canada’s northern permafrost region. The project will focus on the use of eddy co-variance techniques to assess the greenhouse gas and energy balance of northern permafrost peatlands affected by wildfire. Field research will be conducted in northernmost Alberta at sites with established infrastructure.
Impacts of meteorology and management on the carbon, nitrogen and net GHG balance of grasslands, including emissions of atmospheric ammonia (NH3) by cattle grazing .
2 Postdoc Positions at SLU, Umeå, Sweden, are available for exploring the effects of forestry on drained boreal soils on GHG fluxes using eddy covariance and chamber techniques.
Despite the strong power of deep learning techniques on extracting patterns from large datasets and the concurrent realization of the high uncertainty in C stock estimations there is a lack of well established methods in acquiring and processing Lidar point cloud data of high density with deep learning for accurately monitoring ecosystems’ carbon dynamics. The MapCland project targets exactly this line of research and applies an interdisciplinary approach building upon the combined competences of team partners from the Department of Geosciences and Natural Resource Management (IGN) and the Department of Computer Science (DIKU) at the University of Copenhagen. The aim is to initiate new groundbreaking research by applying and extending deep learning methods to explore relevant information from large datasets from Lidar. For further details please read the attached pdf.
PhD opportunity on the environmental impacts of land-based climate mitigation measures, as part of the CLAND ‘Convergence Institute’ in the Paris (France) area. The project will review current evidence of the environmental and ecological impacts of land-based mitigation measures (eg conservation agriculture, agroforestry, forest management or bioenergy production), and provide quantitative assessments by means of meta-analyses.