Project overview:

• Assess how climate change projections affect the viability of index-based insurance schemes for smallholder farmers.
• Quantify changes in indemnity frequency and basis risk under future climate scenarios (CMIP6), using observed and remote-sensing data.

Responsibilities / Tasks:

• Ingest and preprocess large-scale climate projections (CMIP6) and historical weather data (CHIRPS), and vegetation indices (MODIS NDVI).
• Build and evaluate index-based insurance triggers and simulate payouts; estimate crop-yield relationships using FAO crop yield statistics.

Technical environment & tools:

• Primary languages: Python or R.
• Libraries/tools: xarray / netCDF4 for climate data, Prophet or ARIMA for time series modeling, Matplotlib / Seaborn for visualization, Jupyter Notebooks + Git for documentation and reproducibility.

Methodology & analysis:

• Compute relevant climate indicators (e.g., rainfall deficits, cumulative anomalies) from CHIRPS and CMIP6 outputs; bias-correct or downscale as necessary.
• Model historical yield-weather/NDVI relationships (statistical models or machine learning) to link index triggers to expected losses and estimate basis risk.

Deliverables & expected outputs:

• Reproducible Jupyter notebooks and scripts to process data, run simulations, and produce figures.
• A technical report documenting methodology, results, sensitivity to model choices, and recommendations for index design under future climates.

Candidate profile & skills:

• Good programming skills in Python or R and experience with handling netCDF / xarray or equivalent.
• Knowledge of time series modelling (Prophet, ARIMA) and experience with geospatial / remote sensing datasets (MODIS NDVI, CHIRPS).

Application:

• To apply, send your CV, motivation letter and example code/notebooks to stages@hydatis.fr. You can also consult the company website: https://www.hydatis.com
• Use the email subject: "Application Clim-05: Assessing the Impact of Climate Change on Index-Based Insurance (PFE)" and indicate your preferred programming language (Python or R) and availability.