Ecosystem Development Specialist

This is a challenging opportunity for an experienced environmental modeller to join our growing team. Working alongside the Principal Soil Modeller, you will be responsible for developing, implementing, and maintaining 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.

You will need to be adaptable - capable of working independently and as a key member of a multi-disciplinary team reflecting engineering, GIS, soil science, quality management, and data systems, and the commercial team, as well as collaborating effectively with external partners.

Key responsibilities include:

• Model Components & Integration:
  • Working with agricultural ecosystem models including plant growth models (LINTUL-5, LINGRA), soil organic carbon models (RothPC, 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
• Technical Development:
  • Bayesian Framework Development: Contributing to the development and maintenance of the Bayesian data assimilation framework that underpins the AEM, ensuring robust uncertainty quantification and model calibration
  • Model Development: Configuring, running, and extending existing model components such as LINTUL-5 (arable crops), LINGRA (grass), RothPC-N (soil organic carbon and nitrogen)
  • Machine Learning Integration: 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
• Data & Validation:
  • Model Validation: Designing and implementing automated testing frameworks to validate and improve model performance, ensuring statistical rigour in all implementations
• Communication & Documentation:
  • Technical Documentation: Producing comprehensive technical documentation, code comments, and user guides for all modelling implementations
  • 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, including partners and stakeholders

Skills and experience required include:

• 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
• Educational Background: 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 skills include:

• Experience with Bayesian methods and data assimilation frameworks
• Familiarity with Soil carbon models (e.g. RothC) and crop growth models (e.g. LINTUL, WOFOST, DSSAT, APSIM) or grassland models (e.g. LINGRA)
• 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)