EcoSystem Modelling Software Engineer (Remote)

Remotestar; Cambourne; Remote; GBP 40,000 - 65,000; Full time

Job summary

A leading environmental consultancy is seeking an experienced Environmental Modeller to join their team. You will develop and maintain components of the Agricarbon Ecosystem Model (AEM) using advanced Python programming and work with various agricultural ecosystem models. Ideal candidates should have a Master's or PhD in related fields and extensive experience in environmental modelling and data science techniques.

Qualifications

• Extensive experience in Python programming for data science and environmental modelling.
• Proven experience developing and working with ecosystem models.
• Experience with software development best practices including version control.

Responsibilities

• Develop and maintain components of the Agricarbon Ecosystem Model (AEM).
• Implement integration between AEM components for seamless data flow.
• Contribute to Bayesian data assimilation framework development.

Skills

Python programming

Data science

Machine learning techniques

Problem-solving

Version control (Git)

Education

Master's degree or PhD in Data Science

Environmental Science

Computer Science

Tools

NumPy

SciPy

Pandas

scikit-learn

GeoPandas

Job description

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)