EcoSystem Modelling Software Engineer

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August 21

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RemoteStar

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B2B • Recruitment • SaaS

RemoteStar is a global recruitment service that specializes in hiring top-quality tech talent. By assembling diverse teams with vetted developers from various regions, RemoteStar ensures high-quality staffing while maximizing cost efficiency for companies. The service includes a rigorous vetting process, technical matching, and full onboarding support, allowing businesses to focus on their core operations while RemoteStar handles the administrative aspects of recruitment and team management.

11 - 50 employees

Founded 2020

🤝 B2B

🎯 Recruiter

☁️ SaaS

📋 Description

• 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

🎯 Requirements

• 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)