Research Assistant Groundwater Groundwater Modelling

Job Description

The Department of Civil and Environmental Engineering at the United Arab Emirates University is seeking a Research Assistant (02 positions) in the area of groundwater resources and climate resilience. The research assistant will contribute to developing a high-resolution gridded groundwater dataset for the UAE using advanced remote sensing and machine learning techniques. The candidate should be able to integrate in-situ and satellite observations, including GRACE data, to estimate groundwater storage variations and apply machine learning models to improve groundwater predictions. The preferred candidate should have a Bachelors (Honours) degree in a relevant field and demonstrated experience in hydrology, remote sensing, climate data analysis, and machine learning applications. A solid background in climate change analysis (including rainfall variability effects on catchment hydrology), and big-data processing is highly desirable. The candidate should hold at least a Master’s degree in Civil Engineering, specialized in water resource engineering/management, geoinformatics, or a related field, with a proven ability to conduct independent research and publish in high–impact factor journals.

Minimum Qualification

• Bachelors (Honours) degree in a relevant field and a Master’s degree in water resources engineering/management, geoinformatics, or a closely related field.
• Demonstrated knowledge and research experience in hydrology, remote sensing, and climate data analysis.
• Proficiency in data processing, statistical analysis, and machine learning techniques relevant to groundwater studies.
• Knowledge in GIS software and applications and programming/coding in R, Python, etc., with knowledge in big data/climate data handling.
• Proven ability to conduct independent research and publish in peer-reviewed journals.
  
  

• Experience in remote sensing data analysis.
• Strong background in evaluating climate change impacts on water resources, particularly rainfall variability and catchment hydrology.
• Demonstrated expertise in applying advanced machine learning models to hydrological and environmental datasets.
• Experience in working with surface water/groundwater modelling and programming/coding (Python, etc.)
• Track record of publishing research in high–impact factor journals and contributing to collaborative, interdisciplinary projects.