Ecosystem Ecologist Software Specialist

2 days ago

Bareilly, Uttar Pradesh, India beBeeEnvironmental Full time ₹ 40,00,000 - ₹ 50,00,000
Environmental Modelling Software Engineer Role

We are seeking an experienced environmental modeller with strong programming expertise to join our growing team. This is a fantastic opportunity for individuals who wish to make a significant impact in the field of environmental modelling.

The successful candidate will be responsible for developing, implementing, and maintaining components of the Agricarbon Ecosystem Model (AEM) using Python. They will work closely with our Principal Soil Modeller to translate scientific requirements into robust technical solutions.

Main Responsibilities:

  • Develop and implement model components including plant growth models (LINTUL-5, LINGRA), soil organic carbon models (RothPC, RothPC-N), soil water models, mineral nitrogen models, and grazing models.
  • Implement and maintain the integration between different AEM components, ensuring seamless data flow between plant growth, soil carbon, water, nitrogen, and livestock models within the Bayesian data assimilation framework.
  • Contribute to the development and maintenance of the Bayesian data assimilation framework that underpins the AEM, ensuring robust uncertainty quantification and model calibration.
  • Configure, run, and extend 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.
  • Evaluate and implement machine learning and statistical models using Python libraries to enhance overall accuracy and predictive power, potentially as part of ensemble modelling approaches.

Key Requirements:

  • Advanced programming skills in Python, with extensive experience in data science and environmental modelling.
  • Proven experience developing and working with ecosystem models or related areas.
  • Data science proficiency, with extensive experience with machine learning techniques and their application to environmental data, including model validation and statistical analysis.
  • Code quality focus, with experience with software development best practices including version control (Git), testing frameworks, and code documentation.
  • Excellent analytical and problem-solving abilities, with extreme attention to detail and a rigorous approach to model development.

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.
  • Familiarity with data assimilation using satellite-derived data (e.g. Leaf area index, canopy cover).
  • Experience with cloud computing platforms for large-scale data processing (AWS, Azure, GCP).
  • Track record of peer-reviewed publications in relevant fields.
  • Geospatial data handling experience (e.g., GeoPandas, DuckDB, etc.).
  • Familiarity with containerisation and deployment technologies (Docker).

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