The successful candidate will conduct research in the field of water resources and extensive field experiments, with several areas in CARC. Will also assist PI with data downscaling, predictions and evaluation.
Conducts research in the field of water resources and field experiments, with potential applications to hydrological modeling, climate forecasting, and water resources management at CARC
Assist PI with the climate data downscaling, prediction and evaluation; conducting research on impacts of land use/cover changes on eco-hydrologic processes, agricultural water resources management and evaluation of decision support systems for agricultural and landscape settings
Assist PI on irrigation and nutrients best management practices for sustainable use of natural resources and environmental protection in a changing climate, and numerical modeling using spatial data
Assist the principal investigator (PI) in ongoing research projects (experimental and modeling) related to spatial hydrology, remote sensing, drought monitoring, and field experiments
Assist in writing research reports, manuscripts, grants, present results at scientific meetings. Participate in extension and outreach activities as needed by the program
Work in collaboration with other researchers in CARC
Assist undergrad and graduate students, and research technicians with research
Other duties as assigned and needed
Required Education & Experience:
Ph.D. in Hydrology, Civil Engineering, Geosciences, or Agricultural Engineering or any other relevant academic field emphasizing in hydrology.
No prior experience required
Solid record of scholarship and strong written and oral communication skills; strong quantitative, and programming experiences in R, Python, C, C++, and MATLAB.
Preference will be given to candidates who have considerable experiences in spatial data analysis, laboratory analytical techniques, field research design and project implementations at different scales, strong numerical modeling, and statistical skills.
Should have interest in water quality and downscaling of climate projections.