Jobs:Job-00694

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Tenure-track staff scientist position - Remote Sensing and Earth System Modelling
Helmholtz Centre for Environmental Research (UFZ), , Germany
Apply before: 22 June 2022


About the position

The Department of Remote Sensing in UFZ (www.ufz.de/remote-sensing) is seeking to appoint a highly motivated research scientist who is expected to contribute to, but not limited to, one of the following topics:

  1. Explore novel model-data fusion approaches to integrate multi-source remote sensing data and land surface modelling framework, to better quantify water cycle dynamics (e.g., evaporation, soil moisture, precipitation, water levels) from regional to global scales.
  2. Improve the understanding of land-atmosphere feedbacks and hydro-climatic extremes through novel use of remote sensing datasets (e.g., detection of extreme events, impacts of extremes on water cycle and ecosystems).
  3. Apply machine learning methods to improve monitoring and modelling (e.g., fusing and downscaling multi-source remote sensing products, uncertainty quantification, hybrid modelling, causal inference).

More details and application: https://recruitingapp-5128.de.umantis.com/Vacancies/2457/Description/2


About the employer
The Helmholtz Centre for Environmental Research (UFZ) with its 1,100 employees has gained an excellent reputation as an international competence centre for environmental sciences. We are part of the largest scientific organisation in Germany, the Helmholtz association. Our mission: Our research seeks to find a balance between social development and the long-term protection of our natural resources. The Department of Remote Sensing in UFZ (www.ufz.de/remote-sensing) conducts innovative research to advance the understanding of the Earth system via various remote sensing techniques. It has extensive research experience in quantifying land surface dynamics from multi-source Earth observations across scales. Another focus lies on the investigation of land-atmosphere interaction and climate extremes using novel remote sensing products. The department is also involved in the Remote Sensing Centre for Earth System Research (RSC4Earth, https://rsc4earth.de), which is a joint initiative between Leipzig University and UFZ. We will work closely with national (e.g., iDiv, TROPOS, DLR, MPI-BGC, MPI-M, LMU) and international (e.g., NASA, ESA, UKCEH, ECMWF, Oxford University, Princeton University) collaborators to better understand ecosystem functioning and water dynamics in a changing climate.


Preferred Skills

  • PhD degree in remote sensing, meteorology, hydrology, physical geography, environmental sciences, physics, statistics, computer sciences, or related field.
  • Good programming skills (e.g., Python, R, Matlab, or Fortran).
  • Good communication skills in English, and strong interest to work in an interdisciplinary research team.
  • Good record of peer-reviewed publications or successful experience in third-party funding application is an asset.

Our offer

  • World-class research environment with excellent technical facilities
  • Work in inter-disciplinary, multinational teams and excellent links with national and international research networks
  • Provide a range of training and education opportunities to support your career development, particularly the establishment of your own researcher group
  • A vibrant region with a high quality of life and a wide cultural offering for a balance between family and professional life
  • 100% full time position limited for 3 years with the possibility to extend or turn into a permanent position after evaluation
  • Remuneration at public-sector pay grade TVöD E13 including attractive public-sector social security benefits.

To Apply: https://recruitingapp-5128.de.umantis.com/Vacancies/2457/Description/2


Prof. Dr. Jian Peng
Department Remote Sensing
Helmholtz Centre for Environmental Research - UFZ
Permoserstraße 15, 04318 Leipzig, Germany
E-mail: jian.peng@ufz.de

Web: https://www.ufz.de/index.php?en=47410

Of interest for:
  • Hydrology Focus Research Group
  • Artificial Intelligence & Machine Learning Initiative