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2 weeks ago
This is an exciting opportunity for an experienced environmental modeller with strong programming expertise to work on the development, implementation, and maintenance of components of the Agricarbon Ecosystem Model (AEM) using Python.
Key Responsibilities- Model Development:
- Working with agricultural ecosystem models including plant growth models, soil organic carbon models, 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
- Technical Expertise:
- 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 (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 Application:
- 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
- Code Quality & Collaboration:
- Ensuring all modelling code meets high standards for reliability, performance, and maintainability, with comprehensive testing and documentation
- Working closely with a Principal Soil Modeller to translate scientific requirements into robust technical solutions, providing programming expertise to support complex modelling challenges
- Data Validation & Documentation:
- Designing and implementing automated testing frameworks to validate and improve model performance, ensuring statistical rigour in all implementations
- Producing comprehensive technical documentation, code comments, and user guides for all modelling implementations
- Communication:
- Communicating technical modelling concepts and results to both technical and non-technical audiences, including partners and stakeholders
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