Job Description

Position Summary:

The Postdoctoral Researcher will develop, integrate, test, and evaluate different representations of leaf-scale C4 photosynthesis within the Community Land Model (CLM) framework using CLM-FATES. The researcher will compare model performance against observational data at leaf, site, and global scales under varying environmental conditions to identify uncertainties and develop evidence-based recommendations for improved and scalable C4 photosynthesis models. Based in Colorado State University’s Ecological Climate and Global Change lab, the researcher will work under the supervision of Dr. Danica Lombardozzi in the Department of Ecosystem Science and Sustainability.

For full consideration, please submit your application by December 14, 2026, 11:59 PM (MT). Position will remain open until filled.

Essential Job Duties:

• Develop and integrate C4 photosynthesis models in CLM-FATES, including a widely used semi-empirical approach, a mechanistic approach that improves light response, and an optimality approach based on least-cost theory.
• Evaluate model performance at leaf, site, and global scales using available observational data to assess how accurately the different approaches represent C4 photosynthesis.
• Design and conduct model experiments under varying environmental conditions to compare model responses and identify differences among approaches.
• Manage and analyze observational data and model outputs using Python, R, Fortran, or similar programming languages to support reproducible comparisons and evaluation.
• Identify sources of uncertainty and develop evidence-based recommendations for improved and scalable C4 photosynthesis models, including identifying regions most sensitive to model assumptions.
• Perform other related duties as assigned.

Conditions of Employment:

• Successful completion of a pre-employment background check is required.
• A valid U.S. work authorization.

Supervision:

• This position has no direct supervisory responsibilities.

Minimum Qualifications:

• A PhD in ecosystem science, Earth system science, or a related discipline.
• Experience coding in Python, R, Fortran, or similar programming languages.

Preferred Qualifications:

• Strong background in photosynthetic theory.
• Understanding of and experience using photosynthesis models.
• Familiarity and/or experience with ecosystem-scale models.

Additional Information:

The cover letter should include information about your experience with coding, photosynthesis models, and ecosystem-scale models.

Search Contact: Danica Lombardozzi, danica.lombardozzi@colostate.edu .

Salary Range

Required Application Documents

To apply, please upload the following applicant documents. Ensure your materials fully address the required and preferred job qualifications of the position. 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.

Employee Benefits

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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.

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.