Ecosystem Modelling Environmental Specialist

21 hours ago

Surat, Gujarat, India beBeeEnvironmental Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Role Overview

This is an exciting opportunity for a skilled environmental modeler with strong programming expertise to contribute to our team.

Working alongside our principal soil modeler, 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 modeling 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 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
  1. 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: 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
  • Machine Learning Integration: Evaluating and implementing machine learning and 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: Working closely with our Principal Soil Modeller to translate 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: Supporting collaborative research initiatives by providing technical implementation of novel modelling approaches and contributing to peer-reviewed publications
  • Stakeholder Communication: Communicating technical modelling concepts and results to both technical and non-technical audiences, including partners and stakeholders

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