WHAT YOU WILL DO & LEARN

• Model Soil Carbon & GHGs: Learn to set up, calibrate, and validate process-based models (such as DayCent, DNDC, or DSSAT) to estimate soil carbon sequestration and emission reductions across agricultural landscapes.

• Master Carbon Standards: Gain hands-on experience with leading carbon registries and methodologies (e.g., Verra VM0042 and VMD0053), covering structural validation, uncertainty quantification, and Model Validation Report (MVR) development.

• Build Scalable Pipelines: Write reproducible, production-grade Python code and geospatial workflows to scale our models across regions.

• Collaborate & Defend Scientific Models: Help prepare rigorous technical documentation, data visualizations, and scientific rebuttals for third-party auditors (VVBs) and carbon market evaluators.

WHAT WE ARE LOOKING FOR

Educational Background

• Bachelor’s or Master’s degree in Computer Science (B.Tech / M.Tech preferred), Applied Mathematics, Computational Biology, or related fields.

Core Skills

• Strong logical and analytical thinking, and effective technical communication.

Core Foundations

• Production-grade Python, Git version control, statistical modeling, and geospatial data handling.

Coding Toolkit

• Hands-on experience with Python libraries and geospatial tools (e.g., GeoPandas, Rasterio) or GIS tools.

BONUS POINTS (NICE-TO-HAVES)

• Prior exposure to biogeochemical models (DayCent, DNDC, DSSAT) through coursework, research, or internships.

• Familiarity with Bayesian statistics, machine learning surrogates, or remote sensing data (Sentinel-2, MODIS).

• Interest in agricultural systems across India, Southeast Asia, or Sub-Saharan Africa.

• Habit of using modern AI/LLM-assisted workflows to accelerate research, data processing, and code development.