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PhD opportunity - Examining evapotranspiration partitioning across ecosystems

Funded PhD position at University College Dublin - Ireland

 

Title: Examining evapotranspiration partitioning across ecosystems

Project Description: Water loss through transpiration from terrestrial ecosystems plays a crucial role in the global water cycle. Understanding the relative contribution of transpiration (T) and evaporation (E) is essential to improve our understanding of the water cycle and our ability to predict its response to climate change. While measurements of the combined flux of E plus T (evapotranspiration, ET) are numerous, information about the relative contribution of T versus E is not readily available at the ecosystem scale. The important challenge this project aims to address is to determine T and E at ecosystem level for a range of ecosystems. In this unique project we will combine the strength of big data based computer science tools with the deep insights of field based biosphere–atmosphere exchange measurements.

The PhD student will apply a variety of E vs T partitioning methods to existing eddy covariance datasets to determine strengths and constrains of these methods. They will investigate the resulting E and T fluxes to improve our understanding of global transpiration patterns across a range of ecosystems.

The project will provide opportunities to work with collaborators around the globe, including the possibility to collect validation data during a research visit with the AgMet lab of Prof. Dr. Wagner-Riddle at the University of Guelph, Canada.

Qualifications: Minimum educational background: BSc (honours) or MSc degree at 2.1 grade or above (or equivalent) in Environmental Science/Atmospheric Physics/Biological Sciences/Environmental Chemistry/Plant Biology/Soil Science or related discipline. Strong computational skills and experience with programming in Matlab, R, and/or Python (or similar) are required.

Desirable: experience with eddy covariance measurements (field work and/or data analysis) or other trace gas flux measurements, experience with application of machine learning tools to atmosphere – biosphere exchange data

The successful candidate should be enthusiastic, self-motivated and willing to learn new tools and technologies. As part of this PhD the candidate will be expected to demonstrate/assist in undergraduate practicals for the academic session.

Stipend: The student will receive a tax-free stipend of €18,000 per year, full coverage of tuition fees and funds for conference travel. As part of the agreement, the student will be required to serve as demonstrator (Teaching Assistant) for a set number of hours each year, which will be paid on top of the stipend at the hourly rate (https://www.ucd.ie/hr/pay/hourlypaidemployees/hourlypaidrates/).

Equality and diversity: UCD is committed to creating an environment where diversity is celebrated and everyone is treated fairly regardless of gender, age, race, disability, ethnic origin, religion, sexual orientation, civil status, family status, or membership of the travelling community (https://www.ucd.ie/equality/). Applications from all suitably qualified candidates will be considered.

About UCD: UCD, located in the cosmopolitan city of Dublin, Ireland, is one of the top universities in Europe - and is also ranked in the top 1% of higher education institutions worldwide. Our students love the UCD campus, a huge, spacious campus with lakes, woodland walks and wildlife close to Dubin's city centre.
UCD is the most international university in Ireland and welcomes hundreds of new international students every year. Specific information and support for international applicants including visa requirements is available here: https://www.ucd.ie/global/study-at-ucd/

Informal enquiries are welcome and should be made to Dr Elke Eichelmann (elke.eichelmann@ucd.ie).

To apply please e-mail elke.eichelmann@ucd.ie a single pdf document with

  • a detailed curriculum vitae describing any previous research experience,
  • a cover letter detailing your research interests and goals, and
  • the contact details (e-mail and phone number) of at least two academic referees.

Please reference “PhD Application – ET partitioning” in the subject line of the email.

The deadline for applications is Tuesday 1st November 2022.