EcoSystem Modelling Software Engineer; Remote

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 (Num Py, Sci Py, Pandas, scikit-learn, Geo Pandas) 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., Geo Pandas, DuckDB, etc.)
  Familiarity with containerisation and deployment technologies (Docker)