EcoSystem Modelling Software Engineer (Remote)

United Kingdom·CambourneRemotemid

Software EngineerSoftware Engineering

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Quick Summary

Overview

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…

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…

Requirements Summary

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…

Technical Tools

awsazuredockergcpnumpypandaspythonscikit-learnmachine-learningstatistical-modeling

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.

Responsibilities

~1 min read

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

Requirements

~1 min read

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)