In the framework of the Marie Sklodowska-Curie Innovative Training Network (MSCA-ITN-ETN) TRuStEE: Training on Remote Sensing for Ecosystem modElling, we are now recruiting one ESR for 34 months at Fondazione Edmund Mach, San Michele all'Adige (Trento, Italy), who will be part of a network of 12 ESR enrolled in a European network of academic and private institutions.
The candidate will be employed for 34 months at Fondazione Edmund Mach under the supervision of Dr. Loris Vescovo and enrolled in a PhD program.
Ecosystem functional properties strongly influence ecosystem processes, and their spatial and temporal characterization is rather challenging. Plant leaf traits are able to provide crucial information towards the understanding of the ecosystem functional properties and the mechanisms underlying the provision of ecosystem services.
Within the same functional type, extreme differences can be observed in plant traits (such as Leaf Area Index, leaf pigment content, leaf water and nitrogen content, green ratio).
Plant leaf traits in different grassland associations vary in response to environmental, anthropogenic and climatic factors. In mountane environments, the compression of the climatic life zones mostly due to altitudinal variations leads to an extreme spatial variability of plant traits and ecosystem functional properties.
Hyperspectral remote sensing can provide useful data for plant leaf traits characterisation and mapping, providing valuable inputs to the ecosystem modelling community.
The variability of plant leaf traits within the grassland functional type and their ability to provide information on both the ecosystem functional properties and the ecosystem services will be assessed along an altitudinal gradient, ranging from the low altitude temperate meadows up to the subalpine grasslands and alpine tundra ecosystems.
Radiative Transfer Model inversion will allow grassland plant leaf traits estimation (LAI, canopy height, leaf nitrogen and water content) within different grassland canopies. The advantages of integrating LiDAR and hyperspectral data will be investigated, and the impact of canopy structure (spatial distribution of photosynthetic and non photosynthetic canopy elements) on the reflectance response and on the models' ability to estimate plant leaf traits and functional properties.
To apply for the position, please email the following documents to firstname.lastname@example.org and email@example.com by the deadline: 31th August 2017.
- A curriculum vitae, including contact details, education (at University level and other), work experience, prizes/awards, language skills, etc… (max. 2 pages). The CV should reflect a representative array of achievements and qualifications appropriate to the post for which application is being made.
- Official academic record of undertaken courses & grades for Bachelor (and Master if required in specific criteria) degree.
- A motivational letter in which the applicant describes his or her motivation to pursue postgraduate studies and to conduct the research project/s applied for. Mention the ESR project number or numbers (in the latter indicate order of preference if any) on your motivational letter and on the subject of the email.
- Contact details of three referees willing to write confidential letters of recommendation.
Short-listed candidates will be contacted by September 15th for an interview.
Applications received shortly after the deadline are likely to be considered. Priority will be given to applications received before 31st of August 2017.
More information, Secondments, Eligibility Criteria and Benefit in the following Euraxess webpage.
Information available also at Fondazione Mach "Work with us" webpage.