Postdoctoral Research Associate, Electrical Resistivity Tomography and Environmental Geophysics

Postdoctoral Research Associate, Electrical Resistivity Tomography and Environmental Geophysics Posting Number req26727 Department Biosphere 2 Department Website Link https://biosphere2.org/ Location Tucson Campus Address Tucson, AZ USA Position Highlights

The Biosphere 2 Landscape Evolution Observatory (LEO) at the University of Arizona seeks an enthusiastic scientist or engineer to lead development and operation of one of the world's most advanced environmental Electrical Resistivity Tomography (ERT) observatories.

LEO consists of three instrumented, 330 m² experimental hillslopes designed to study the coupled evolution of hydrology, weathering, ecosystems, and biogeochemistry. The successful candidate will advance ERT as a central observational platform for monitoring water movement, subsurface structure, weathering, root-zone dynamics, and ecosystem development in real time.

The position offers exceptional opportunities to combine environmental geophysics, hydrology, soil science, ecology, geochemistry, and artificial intelligence through collaboration with researchers at Biosphere 2 and across the University of Arizona.

The successful candidate will help answer questions such as:

• How does rainfall redistribute through evolving landscapes?
• How do plant roots alter subsurface hydrology?
• How do weathering and biological activity modify subsurface electrical properties?
• Can geophysical observations be assimilated into predictive ecosystem digital twins?
• Can autonomous AI systems optimize environmental monitoring and experimentation?

Visa sponsorship is not available for this position.

Outstanding U of A benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; U of A/ASU/NAU tuition reduction for the employee and qualified family members; access to U of A recreation and cultural activities; and more!

The University of Arizona has been recognized for our innovative work-life programs. For more information about working at the University of Arizona and relocations services, please click here.

Duties & Responsibilities

• Lead operation and maintenance of permanent ERT instrumentation within the Landscape Evolution Observatory.  
• Design and execute new ERT experiments supporting rainfall, vegetation, and ecosystem manipulation studies in collaboration with an interdisciplinary science team. 
• Develop automated data acquisition, calibration, and quality assurance workflows.  
• Develop inversion methods for estimating spatial and temporal changes in soil moisture and subsurface properties.  
• Integrate ERT observations with complementary sensor networks and environmental observations.  
• Collaborate on coupling geophysical observations with mechanistic ecosystem, hydrological, and reactive transport models (e.g., EcoSIM, PFLOTRAN, CATHY) a part of the new DOE funded Genesis project at Biosphere 2.  
• Develop new approaches using machine learning and AI to improve inversion, interpretation, and autonomous monitoring.  
• Publish peer-reviewed research and contribute to external funding proposals.  
• Mentor graduate students, undergraduate researchers, and visiting scientists.  

Knowledge, Skills, & Abilities: 

• Strong quantitative and analytical skills.  
• Excellent written and oral communication skills.  

Minimum Qualifications

• Ph.D. (or M.S. for Research Technologist) in Geophysics, Hydrology, Geological Engineering, Environmental Engineering, Earth Science, Physics, or related discipline.  
• Experience with Electrical Resistivity Tomography.  
• Programming experience (Python, MATLAB, Julia, or similar).  

Preferred Qualifications

• Experience with time-lapse electrical resistivity tomography (ERT) inversion techniques.
• Experience in hydrogeophysics.
• Experience with soil physics and/or vadose-zone hydrology.
• Experience designing, deploying, or maintaining environmental sensor networks.
• Experience with reactive transport modeling and/or hydrologic modeling.
• Experience applying machine learning or artificial intelligence (AI) methods to environmental, hydrologic, or geophysical research.
• Experience working collaboratively on interdisciplinary field experiments.

FLSA Exempt Full Time/Part Time Full Time Number of Hours Worked per Week 40 Job FTE 1.0 Work Calen