Healthcare SMEs evaluating clinical accuracy, reasoning, and safety in AI - generated medical content for Volga Partners. Project - based remote assignments for physicians, nurse practitioners, and physician assistants.
About the role
- 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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