
Ecosystem Dynamics and Simulation Specialist
2 days ago
A seasoned Environmental Modeller is sought after to develop and maintain components of the Agricarbon Ecosystem Model (AEM) using Python.
Working closely with a Principal Soil Modeller, you will be responsible for designing and implementing automated testing frameworks to validate and improve model performance. 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.
Key responsibilities include:
Model Components & Integration- Working with agricultural ecosystem models including plant growth models such as LINTUL-5 and LINGRA, soil organic carbon models like RothPC and RothPC-N, soil water models, mineral nitrogen models, and grazing models
- Implementing and maintaining 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
- Contributing to the development and maintenance of the Bayesian data assimilation framework that underpins the AEM, ensuring robust uncertainty quantification and model calibration
- Configuring, running, and extending existing model components such as LINTUL-5 and LINGRA, developing Python implementations that maximise the benefit of our access to the world's largest soil carbon database
- Evaluating and implementing machine learning and statistical models using Python libraries to enhance overall accuracy and predictive power, potentially as part of ensemble modelling approaches
- Ensuring all modelling code meets high standards for reliability, performance, and maintainability, with comprehensive testing and documentation
- Working closely with our Principal Soil Modeller to translate scientific requirements into robust technical solutions, providing programming expertise to support complex modelling challenges
- Designing and implementing automated testing frameworks to validate and improve model performance, ensuring statistical rigour in all implementations
- Mandatory requirements:
- 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
- Code Quality Focus: Experience with software development best practices including version control (Git), testing frameworks, and code documentation
- Problem-Solving Skills: Excellent analytical and problem-solving abilities with extreme attention to detail and a rigorous approach to model development
- Nice-to-have qualifications:
- Experience with Bayesian methods and data assimilation frameworks
- Familiarity with integrated agricultural system models
- Knowledge of nitrogen cycling and soil-plant-atmosphere interactions
- Geospatial data handling experience (e.g., GeoPandas, DuckDB, etc.)
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