Job Title: Assistant Research Scientist
Agency: Texas A&M Agrilife Research
Department: Temple
Proposed Minimum Salary: Commensurate
Job Location: Temple, Texas
Job Type: Staff
Job Description
The selected candidate would assist a team of ecosystem modeling scientists to perform process-based ecosystem models based research and develop decision support systems. The candidate would also assist in writing python/R programming scripts to optimize processing time, making the current decision support systems more efficient and reliable.
Responsibilities:
- Aids in the development and application of national scale modeling frameworks for water and crop management. Prepare and process large datasets for use in national model assessments conducted with SWAT/SWAT+.
- Aids in the development of software and tools to support large- and small-scale modeling efforts.
- Integrates ML/AI with Process-based modeling to develop/optimize decision support systems
- Works effectively within a modeling group comprised of ARS, AgriLife, and other personnel. Actively participates in joint research activities. Effectively manages and completes assigned tasks.
- Other job-related duties as required.
Required Education and Experience:
- Ph.D. in Ecology, Hydrology, Geology, Environmental Science/Engineering, Civil Engineering, or related disciplines with emphasis on surface hydrology or watershed assessment.
- Relevant professional experience.
Required Knowledge, Skills, and Abilities:
- Demonstrated publication record in hydrological or water quality modeling and analysis.
- Interdisciplinary teamwork skills with researchers across different disciplines.
- Flexibility to perform work in a team or individually with supervision.
- Good communication, interpersonal skills, professionalism, and competency.
- Ability to multi-task and work cooperatively with others.
Preferred Knowledge, Skills, and Abilities:
- Knowledge of computer models on watershed assessment.
- Knowledge of programming or scripting languages such as Python, Fortran, R, or others.
- Experience in remote sensing data analysis and geographic information systems.
- Experience in machine learning-motivated algorithms and quantitative data analysis.
- Strong writing and communication skills.
All positions are security-sensitive. Applicants are subject to a criminal history investigation, and employment is contingent upon the institution’s verification of credentials and/or other information required by the institution’s procedures, including the completion of the criminal history check.
Equal Opportunity/Affirmative Action/Veterans/Disability Employer.