Role : This is an exciting opportunity for an experienced environmental modeller with strong programming expertise to join our 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. Key responsibilities: 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 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 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)