Fully Funded PhD Scholarship: Soil-Plant System Modelling – Université catholique de Louvain
About UCLouvain & Earth and Life Institute
Applications are open for a PhD Soil Plant System Modelling Belgium position at UCLouvain’s Earth and Life Institute as part of the Horizon Europe iRIS project. The research focuses on developing AI-based models to transform construction waste into functional soils. The Earth and Life Institute (ELI) at UCLouvain is a research institute that gathers more than 450 scientists, covering a wide range of disciplines in the Earth and life sciences. The aim of their research is to study and understand the fundamental physical, chemical and biological processes that govern the functioning of our planet, in order to develop sustainable solutions to major societal challenges. This research position is part of the EU project iRIS (Intelligent Research Infrastructure Sustainability), funded under HORIZON EUROPE.
Scholarship Overview
Project Summary
The iRIS project proposes a transformative initiative to reduce the environmental and climate footprint of European Research Infrastructures (RIs). By leveraging machine learning (ML) and artificial intelligence (AI), iRIS introduces sustainable solutions targeting the entire life cycle of RIs. The position is embedded in Work Package 4 (WP4), which aims to optimise the reuse of construction waste, particularly excavated materials, by transforming it into functional soils, thereby reducing the environmental footprint of future RIs. The project will utilise data collected at CERN’s OpenSkyLab.
Why This Scholarship Stands Out
This PhD is unique because it combines soil science, plant ecology, and AI to solve a practical environmental problem: transforming construction waste into functional soils. You will work with data from CERN’s OpenSkyLab, one of the world’s most advanced research infrastructures. The project involves developing an AI-based predictive surrogate model to assess soil-plant features including water and nutrient retention, carbon sequestration, biomass productivity, biodiversity enhancement, and microclimate effects. For a student interested in soil science, plant modelling, and machine learning, this is an opportunity to work at the intersection of environmental engineering and AI, with direct applications for reducing the environmental footprint of large-scale infrastructure projects.
Key Tasks
- Conduct a comprehensive literature review on the application of soil-plant models in soil reconstitution programmes
- Develop a methodology for designing an AI-based soil-plant model tailored to evaluate soil functions and ecosystem services in reconstituted soils
- Collect and curate a robust dataset at CERN’s OpenSkyLab to train and validate the model
- Design, implement, and test an AI-driven surrogate soil-plant model to optimise the soil reconstitution process
- Contribute to the drafting and editing of reports for iRIS deliverables related to WP4
- Participate in workshops and conferences organised within the iRIS project
- Support outreach activities for WP4, including dissemination of project results and stakeholder engagement
- Present research findings at seminars and meetings organised by the hosting laboratory
Candidate Profile and Eligibility
| Requirement | Details |
|---|---|
| Education | Master’s degree in Bioengineering, Agricultural Engineering, Environmental Engineering, or closely related discipline |
| Expertise | In-depth knowledge of physical and biological processes governing soil-plant interactions |
| Technical Skills | Proven experience in numerical modelling of complex systems using Python or R |
| Innovation Mindset | Strong willingness to develop and apply machine learning techniques for modelling soil-plant functions |
| Scientific Communication | Commitment to presenting research findings at leading scientific workshops and conferences |
| Collaboration | Excellent communication skills, ability to engage with academic and public stakeholders |
| Language | Fluent written and spoken English (additional languages are a plus) |
What They Offer
| Benefit | Details |
|---|---|
| Funding | Fully funded PhD scholarship (Horizon Europe) |
| Intellectual Freedom | Tackle cutting-edge challenges at the intersection of fundamental research and practical applications |
| Collaboration | Interdisciplinary and international teams |
| Networks | Privileged access to national and international research networks |
| Infrastructure | State-of-the-art research infrastructure and advanced research data management |
| Work-Life Balance | Flexible working hours and comprehensive work-life balance policy |
| Relocation Support | Dedicated support for international colleagues relocating to UCLouvain |
| Location | Dynamic region known for high quality of life, social diversity, and rich cultural scene |
My Application Strategy
- Highlight your soil-plant system knowledge – In-depth understanding of physical and biological processes in soil-plant interactions
- Emphasise numerical modelling experience – Python or R for complex system modelling
- Show interest or experience in machine learning – Developing AI-based surrogate models is core
- Demonstrate ability to work with large datasets – Data collection and curation at CERN’s OpenSkyLab
- Include a support letter from an expert in the domain – Required as part of the application
Who Should Apply
This PhD is perfect for a student with a background in bioengineering, agricultural engineering, or environmental engineering who wants to apply AI to soil-plant systems. If you are interested in how construction waste can be transformed into functional soils, and want to develop predictive models for water retention, carbon sequestration, biomass productivity, and biodiversity, this project offers training across soil science, plant ecology, numerical modelling, and machine learning.
How to Apply
Submit your application by email to Pr. Marnik Vanclooster (Marnik.vanclooster@uclouvain.be) before August 15, 2026, 24:00.
Required documents (single combined PDF file):
One support letter from an expert in the domain
Motivation letter
Detailed CV
Copy of master’s diploma
Certified transcript of master study results
