Ecological Modeller

This is an exciting opportunity for an experienced environmental modeller with strong programming expertise to join a growing team. Working alongside our 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.

• Model Components & Integration:
  • Working with agricultural ecosystem models (AEM) including plant growth models (LINTUL-5, LINGRA), soil organic carbon models (RothPC, RothPC-N), soil water models, mineral nitrogen models, and grazing models
• Model Integration: 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), 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, potentially as part of ensemble modelling approaches
• Code Quality & Collaboration:
  • Code Quality and Maintenance: Ensuring all modelling code meets high standards for reliability, performance, and maintainability, with comprehensive testing and documentation
  • Technical Collaboration: Working closely with our Principal Soil Modeller to translate scientific requirements into robust technical solutions, providing programming expertise to support complex modelling challenges

Must have:

• 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

We offer:

• A collaborative and dynamic work environment
• The opportunity to contribute to cutting-edge research in environmental modelling
• Professional development opportunities
• A competitive salary and benefits package

Preferred qualifications include:

• 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)