Дата проведення: 11/01/10 - 11/30/10
Місце проведення: Germany
Організатор: Max Planck Institute
Type of grant: For studying abroad, For scientificresearches, Scholarships
The research group is dedicated toa better understanding of the role of Vegetation-Soil feedbacks forbiogeochemical cycles within the Earth System. In particular we are focusing onthe interaction between the carbon and water cycles at different spatial andtemporal scales. The integration of knowledge gained by exploring local data(e.g. ecosystem carbon and water flux data, soil profile information) andspatial data (e.g. remote sensing, spatial data bases) into process-baseddynamic models will play a pivotal role in this context.
The PhD candidate will contributeto these issues from a carbon cycle modelling perspective. We are using anddeveloping dynamic global vegetation models which combine representations ofbiogeochemical fluxes and terrestrial vegetation dynamics to address theresponse of ecosystem functions to climate change. The PhD candidate isdedicated to improve such a process-based model with respect to vegetationcarbon processes by making use of remotely sensed information about theecosystem state and function.
Requirements:Key requirements for this PhD position are scientific enthusiasm,strong abilities in quantitative, abstract and system-oriented thinking and theambition to publish results in international peer-reviewed journals (Englishlanguage), as well as advanced practical skills with respect to computer-baseddata analysis and scientific programming. Candidates with background (Diploma,Master, or equivalent) in any quantitative science (e.g. geo-ecology or othergeo-science, environmental and natural sciences, applied mathematics orcomputer science) are eligible for these positions, given their strongmotivation to complement their past training. Experience with at least one ofthe following topics is helpful: system modeling, advanced statistical dataanalysis, vegetation ecology, remote sensing products, forest inventory,ecosystem CO2 and energy flux data.