Environmental Model Developer

1 day ago

Alwar, Rajasthan, India beBeeEcosystem Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Ecosystem Modelling Specialist

This is an exciting opportunity for a seasoned environmental modeller with strong programming expertise.

As a key member of our multi-disciplinary team, you will work alongside our Principal Soil Modeller to develop, implement, and maintain components of the Agricarbon Ecosystem Model (AEM) using Python.

Your advanced programming skills will be crucial in translating complex modelling concepts into robust, production-ready code that enhances our ability to make accurate predictions of soil carbon levels and agricultural system interactions.

The successful candidate will be responsible for:

  • Modelling Components & Integration:
  • Model Components & Integration: Working with agricultural ecosystem models (AEM) including plant growth models (LINTUL-5, LINGRA), soil organic carbon models (RothPC, RothPC-N), soil water models, mineral nitrogen models, and grazing models
  • Integrate different AEM components ensuring seamless data flow between plant growth, soil carbon, water, nitrogen, and livestock models within the Bayesian data assimilation framework
  • Technical Development: Contributing to the development and maintenance of the Bayesian data assimilation framework that underpins the AEM, ensuring robust uncertainty quantification and model calibration

You will need to be adaptable and able to work independently, as well as collaborate effectively with external partners.

We are looking for a highly skilled individual with extensive experience in Python programming for data science and environmental modelling, including proficiency with scientific libraries (NumPy, SciPy, Pandas, scikit-learn, GeoPandas) and Bayesian statistical libraries (PyMC or similar).

The ideal candidate will have a Master's degree or PhD in Data Science, Environmental Science, Computer Science, or related field with a strong focus on modelling and programming.

Nice to have:

  • Experience with Bayesian methods and data assimilation frameworks
  • Familiarity with Soil carbon (e.g. RothC) and crop growth models (e.g. LINTUL, WOFOST, DSSAT, APSIM) or grassland (e.g. LINGRA) models, and/or integrated agricultural system models
  • Knowledge of nitrogen cycling and soil-plant-atmosphere interactions

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