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- Tasks: Develop and maintain innovative agricultural ecosystem models using Python.
- Company: Join a forward-thinking team dedicated to environmental sustainability.
- Benefits: Flexible remote work, competitive salary, and opportunities for professional growth.
- Why this job: Make a real difference in environmental modelling while working with cutting-edge technology.
- Qualifications: Strong Python programming skills and experience in environmental modelling required.
- Other info: Collaborative environment with a focus on innovation and career advancement.
The predicted salary is between 36000 - 60000 £ per year.
Role :This is an exciting opportunity for an experienced environmental modeller with strongprogramming expertise to join our growing team. Working alongside our Principal SoilModeller, you will be responsible for developing, implementing, and maintaining components ofthe 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 watermodels, mineral nitrogen models, and grazing modelsModel Integration : Implementing and maintaining the integration between differentAEM components, ensuring seamless data flow between plant growth, soil carbon,water, nitrogen, and livestock models within the Bayesian data assimilation frameworkTechnical DevelopmentBayesian Framework Development : Contributing to the development andmaintenance of the Bayesian data assimilation framework that underpins the AEM,ensuring robust uncertainty quantification and model calibrationModel Development : Configuring, running, and extending existing model componentssuch as LINTUL-5 (arable crops), LINGRA (grass), RothPC-N (soil organic carbon andnitrogen), developing Python implementations that maximise the benefit of our access tothe world\’s largest soil carbon databaseMust have:
Advanced Programming Skills: Extensive experience in Python programming fordata science and environmental modelling, including proficiency with scientificlibraries (NumPy, SciPy, Pandas, scikit-learn, GeoPandas) and Bayesian statisticallibraries (PyMC or similar)Environmental Modelling Experience : Proven experience developing andworking with ecosystem models or related areasData Science Proficiency : Extensive experience with machine learningtechniques and their application to environmental data, including model validationand statistical analysisCode Quality Focus: Experience with software development best practicesincluding version control (Git), testing frameworks, and code documentationProblem-Solving Skills: Excellent analytical and problem-solving abilities withextreme attention to detail and a rigorous approach to model developmentEducational Background: Master\’s degree or PhD in Data Science,Environmental Science, Computer Science, or related field with a strong focus onmodelling and programmingNice to have:Experience with Bayesian methods and data assimilation frameworksFamiliarity 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 modelsKnowledge of nitrogen cycling and soil-plant-atmosphere interactionsFamiliarity 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 fieldsGeospatial data handling experience (e.g., GeoPandas, DuckDB, etc.)Familiarity with containerisation and deployment technologies (Docker)
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EcoSystem Modelling Software Engineer (Remote) employer: RemoteStar
Contact Detail:
RemoteStar Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land EcoSystem Modelling Software Engineer (Remote)
✨Tip Number 1
Network like a pro! Reach out to folks in the environmental modelling space on LinkedIn or at industry events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a GitHub repository with some of your best Python projects related to ecosystem modelling. This gives potential employers a sneak peek into your coding prowess.
✨Tip Number 3
Prepare for interviews by brushing up on Bayesian data assimilation and the specific models mentioned in the job description. We want you to be ready to discuss how you can contribute to the Agricarbon Ecosystem Model!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets the attention it deserves. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace EcoSystem Modelling Software Engineer (Remote)
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your advanced programming skills, especially in Python. We want to see how you've used libraries like NumPy and SciPy in your previous projects, so don’t hold back!
Tailor Your Application: Customise your application to reflect the specific requirements of the EcoSystem Modelling Software Engineer role. Mention your experience with ecosystem models and any relevant projects that align with our work at StudySmarter.
Be Clear and Concise: Keep your application clear and to the point. We appreciate a well-structured application that makes it easy for us to see your qualifications and experiences without wading through unnecessary fluff.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity with our team.
How to prepare for a job interview at RemoteStar
✨Know Your Models Inside Out
Make sure you’re well-versed in the agricultural ecosystem models mentioned in the job description, like LINTUL-5 and RothPC. Brush up on how they work and be ready to discuss your experience with them, as this will show your genuine interest and expertise.
✨Show Off Your Python Skills
Prepare to demonstrate your programming prowess in Python. Have examples ready that showcase your use of libraries like NumPy and SciPy. You might even want to bring a small project or code snippet to discuss, which can really impress the interviewers.
✨Understand Bayesian Frameworks
Since the role involves Bayesian data assimilation, make sure you can explain what it is and how you've applied it in your past work. Be prepared to discuss any challenges you faced and how you overcame them, as this shows your problem-solving skills.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the team’s current projects or future goals related to the Agricarbon Ecosystem Model. This not only shows your enthusiasm but also helps you gauge if the company is the right fit for you.
