Intern supporting AI-assisted and AEM-conventional hydrologic model calibration. Assisting with model simulations and documentation for environmental monitoring solutions.
Responsibilities
Support an end-to-end comparison study of AI-assisted and AEM-conventional hydrologic model calibration.
Deliver benchmarking data, training materials, and a final recommendation report.
Assist in defining and documenting the AI vs. AEM model comparison framework.
Support the Hydrology Team in running AEM model simulations and collecting AI model results.
Gather, organize, and validate historical hydrometeorological datasets for calibration testing.
Participate in statistical performance analysis comparing auto-calibrated and manually calibrated models.
Document and evaluate the auto-calibration feature in VFLO.
Contribute to the development of training materials and best-practice documentation for AEM hydrologists.
Assist the Project Lead in preparing the final report and recommendation by August 21, 2026.
Requirements
Currently enrolled in a degree program in Hydrology, Civil/Environmental Engineering, Water Resources, or a related field.
Basic understanding of hydrologic modeling concepts and calibration principles.
Familiarity with data analysis and statistical methods.
Proficiency with standard data and document tools (Excel, Word, etc.).
Strong written and verbal communication skills for documentation and reporting.
Ability to work both independently and collaboratively within a multidisciplinary team.
Experience with hydrologic modeling software such as VFLO, HEC-HMS, or similar distributed models.
Exposure to AI/ML concepts or data science methodologies in environmental applications.
Experience handling hydrometeorological datasets.
Coursework or project experience in model calibration and validation.
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