Senior Ecosystem Model Developer

1 week ago

Pushkar, Rajasthan, India beBeeEcosystemDeveloper Full time US$ 1,00,000 - US$ 1,40,000
Ecological Model Developer

This is an exciting opportunity for a seasoned environmental modeller with advanced programming expertise to develop and implement robust ecosystem models.

You will be working on developing, implementing, and maintaining components of the Agricarbon Ecosystem Model (AEM) using Python.

As a key member of a multi-disciplinary team, you will collaborate effectively with external partners to deliver accurate predictions of soil carbon levels and agricultural system interactions.

  • Key responsibilities:
  • Model Development: Developing and integrating different AEM components, ensuring seamless data flow between plant growth, soil carbon, water, nitrogen, and livestock models within the Bayesian data assimilation framework.

Technical Expertise

Python Implementation: Configuring, running, and extending existing model components such as LINTUL-5 (arable crops), LINGRA (grass), RothPC-N (soil organic carbon and nitrogen), developing Python implementations that maximise the benefit of our access to the world's largest soil carbon database.

Machine Learning Integration: Evaluating and implementing machine learning and statistical models using Python libraries to enhance overall accuracy and predictive power.

Collaboration & Documentation

Code Quality & Collaboration: Ensuring all modelling code meets high standards for reliability, performance, and maintainability, with comprehensive testing and documentation.

Research Support: Supporting collaborative research initiatives by providing technical implementation of novel modelling approaches and contributing to peer-reviewed publications.

Stakeholder Communication: Communicating technical modelling concepts and results to both technical and non-technical audiences.

Requirements

Mandatory Skills:

  • Advanced Programming Skills: 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).
  • Environmental Modelling Experience: Proven experience developing and working with ecosystem models or related areas.
  • Data Science Proficiency: Extensive experience with machine learning techniques and their application to environmental data, including model validation and statistical analysis.

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.
  • Knowledge of nitrogen cycling and soil-plant-atmosphere interactions.
  • Geospatial data handling experience (e.g., GeoPandas, DuckDB, etc.).

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