Role :This is an exciting opportunity for an experienced environmental modeller with strongprogramming expertise to join our growing team. Working alongside our Principal SoilModeller, you will be responsible for developing, implementing, and maintaining components ofthe 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 watermodels, mineral nitrogen models, and grazing modelsModel Integration: Implementing and maintaining the integration between differentAEM components, ensuring seamless data flow between plant growth, soil carbon,water, nitrogen, and livestock models within the Bayesian data assimilation frameworkTechnical DevelopmentBayesian Framework Development: Contributing to the development andmaintenance of the Bayesian data assimilation framework that underpins the AEM,ensuring robust uncertainty quantification and model calibrationModel Development: Configuring, running, and extending existing model componentssuch as LINTUL-5 (arable crops), LINGRA (grass), RothPC-N (soil organic carbon andnitrogen), developing Python implementations that maximise the benefit of our access tothe world's largest soil carbon databaseMust have:Advanced Programming Skills: Extensive experience in Python programming fordata science and environmental modelling, including proficiency with scientificlibraries (NumPy, SciPy, Pandas, scikit-learn, GeoPandas) and Bayesian statisticallibraries (PyMC or similar)Environmental Modelling Experience: Proven experience developing andworking with ecosystem models or related areasData Science Proficiency: Extensive experience with machine learningtechniques and their application to environmental data, including model validationand statistical analysisCode Quality Focus: Experience with software development best practicesincluding version control (Git), testing frameworks, and code documentationProblem-Solving Skills: Excellent analytical and problem-solving abilities withextreme attention to detail and a rigorous approach to model developmentEducational Background: Master's degree or PhD in Data Science,Environmental Science, Computer Science, or related field with a strong focus onmodelling and programmingNice to have:Experience with Bayesian methods and data assimilation frameworksFamiliarity 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 modelsKnowledge of nitrogen cycling and soil-plant-atmosphere interactionsFamiliarity 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 fieldsGeospatial data handling experience (e.g., GeoPandas, DuckDB, etc.) Familiarity with containerisation and deployment technologies (Docker) #J-18808-Ljbffr