Research Scientist I

Position Location

Fort Collins, CO

Work Location

Position qualifies for hybrid/in-office work

Research Professional Position

Yes

Posting Number

AP

Position Type

Admin Professional/ Research Professional

Classification Title

Resch Sci/Scholar I

Number of Vacancies

Work Hours/Week

40

Proposed Annual Salary Range

$96,000-$105,000

Employee Benefits

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Desired Start Date

01/12/2026

Position End Date (if temporary)

To ensure full consideration, applications must be received by 11:59pm (MT) on

11/16/2025

Description of Work Unit

The One Water Solutions Institute (OWSI) in the Department of Civil and Environmental Engineering (CEE), provides world-class research and innovative technologies to promote collaboration between CSU and outside partners such as local governments, private sector/industry members and non-profit organizations. OWSI is home to several national research centers with specific efforts underway to address a variety of water sustainability challenges. The fundamental research and resulting products developed by OWSI facilitate adoption of "One Water" strategies with a focus on technological, institutional and financial pathways toward sustainable management of water and linked systems. Additional information can be found at

The CEE Department at CSU is recognized both nationally and internationally for its education, research, and service and outreach programs, and is ranked in the top 40 of such programs in the USA. The CEE Department consists of 36 tenured and tenure-track faculty, 16 research scientists/scholars, 27 research associates, and 11 administrative professionals. Additional information about the CEE Department can be found at

Position Summary

The Department of Civil and Environmental Engineering at Colorado State University seeks a highly motivated Research Scientist to work under the supervision of Prof. Mazdak Arabi. The CSU One Water solutions Institute (OWSI) is recruiting a highly qualified candidate to develop deep learning techniques along with artificial intelligence (AI) using large language models for the following projects:

• Enhancing characterization of trends in large floods and flood forecasting: The Flood Potential Portal, developed by the U.S. Forest Service and OWSI, enhances the understanding of flood variability and quantifies design flood discharges and flood frequency relationships. The software assists practitioners with assessments to support infrastructure decisions, including designing road-stream crossings. This project develops and implements advanced ensemble machine learning to improve forecasting of temporal trends in large floods due to changes in the underlying climatic, physiographic, and ecohydrologic conditions.

• Physics-informed machine learning for agroecosystem modeling: This project funded by USDA focuses on developing deep learning algorithms that incorporate scientific understanding of hydrological, water quality, and soil and crop processes. Specifically, physics-informed techniques will be developed based on the SWAT+ model for agroecosystem modeling at the field to watershed scales. The deep learning model will be trained with the outputs of the calibrated and tested SWAT+ model to represent landscape-level hydrologic and water quality responses. The outputs of this landscape-level model will include crop water use, soil erosion, phosphorus runoff, water quality, and crop yield/productivity. The performance validity of the model will be corroborated using extensive observed hydrologic, water quality, and crop yield data across the study region.

• Urban water modeling software: Over the past decade, OWSI has developed data analytics and modeling software for integrated water and land use planning in cities. Specifically, the Polaris, IUWM, and CLASIC tools provide capacities for water supply-demand assessments, lifecycle assessment of green and gray stormwater practices, and evaluation of fit-for-purpose use of alternative water sources. This project will incorporate advanced machine learning methods and tools in Polaris, IUWM, and CLACIS tools for enhanced characterization and forecasting purposes.

Required Job Qualifications

• Applicants must hold a Ph.D. degree in Civil and Environmental Engineering by the start date.
• Applicants must have demonstrated competence in programming and coding.
• Applicants must have deep expertise and experience with developing and applying deep learning techniques for water infrastructure modeling and analysis.
• Applicants must have demonstrated experience publishing high-impact, peer-reviewed scientific papers in the field of water resources planning and management, agricultural sustainability, and infrastructure resilience planning.

Preferred Job Qualifications

• Experience with the SWAT+ and SWMM models.
• Experience with geoprocessing, spatial modeling, and the ArcGIS software.
• Demonstrated experience working in collaborative research settings.

Essential Duties

Job Duty Category

Research

Duty/Responsibility

• Conduct ensemble machine learning for characterization of trends in large floods and flood forecasting.
• Develop physics-informed machine learning algorithms based on process-based agroecosystems models such as SWAT+.
• Develop spatiotemporal machine learning methods to improve assessments, predictive and forecasting capacity, and scenario planning components of the Polaris, NetZero Water, and CALSIC tools developed by OWSI.

Percentage Of Time

80%

Job Duty Category

Writing and Scholarly Presentations

Duty/Responsibility

• Prepare and submit scientific publications.
• Prepare and present scholarly presentations.
• Participate in grant writing.

Percentage Of Time

20%

Application Details

Special Instructions to Applicants

Interested applicants must submit a cover letter which addresses how professional experiences align with identified required and preferred qualifications of the position, a current CV, and the names, e-mail addresses, and telephone numbers of three (3) professional references. References will not be contacted without prior notification to candidates.

CSU is committed to full inclusion of qualified individuals. If you are needing assistance or accommodations with the search process, please reach out to the listed search contact.

Please note, applicants may redact information from their application materials that identifies their age, date of birth, or dates of attendance at or graduation from an educational institution.

Conditions of Employment

Pre-employment Criminal Background Check (required for new hires)

Search Contact

David Logan;

EEO

Colorado State University (CSU) provides equal employment opportunities to all applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Background Check Policy Statement

Colorado State University strives to provide a safe study, work, and living environment for its faculty, staff, volunteers and students. To support this environment and comply with applicable laws and regulations, CSU conducts background checks for the finalist before a final offer. The type of background check conducted varies by position and can include, but is not limited to, criminal history, sex offender registry, motor vehicle history, financial history, and/or education verification. Background checks will also be conducted when required by law or contract and when, in the discretion of the University, it is reasonable and prudent to do so.