Ecosystem Modelling Software Engineer

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.

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

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:

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

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

maintenance of the Bayesian data assimilation framework that underpins the AEM,

ensuring robust uncertainty quantification and model calibration

Model Development

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

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

scientific requirements into robust technical solutions, providing programming expertise

to support complex modelling challenges

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

implementation of novel modelling approaches and contributing to peer-reviewed

publications

Stakeholder Communication

results to both technical and non-technical audiences, including partners and

stakeholders

Skills and experience:

Must have:

Advanced Programming Skills:

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

working with ecosystem models or related areas

Data Science Proficiency

techniques and their application to environmental data, including model validation

and statistical analysis

Code Quality Focus:

including version control (Git), testing frameworks, and code documentation

Problem-Solving Skills:

extreme attention to detail and a rigorous approach to model development

Educational Background:

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)