Doctoral Scholarship Position in Soil Plant System Modelling

Environmental science » Natural resources management

Organisation/Company Université catholique de Louvain Department Earth and Life Institute Research Field Environmental science » Natural resources management Agricultural sciences » Soil science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Final date to receive applications 15 Aug 2026 - 23:00 (Europe/Brussels) Country Belgium Type of Contract Temporary Job Status Full-time Hours Per Week 40 Offer Starting Date 1 Sep 2026 Is the job funded through the EU Research Framework Programme?

Horizon Europe Reference Number INFRA-2025-01-TECH-01 Is the Job related to staff position within a Research Infrastructure? No

Location.

The job.

This research position is part of the EU project iRIS (Intelligent Research Infrastructure Sustainability), funded under the HORIZON EUROPE INFRA-2025-01-TECH-01. 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—from design and construction to operation and reuse.

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. To achieve this, the project will utilise data collected at CERN’s Open Sky Lab  (openskylab.web.cern.ch). The successful candidate will contribute to the following key tasks: i) Defining the scientific foundation and standard operating procedures (SOPs) for soil reconstitution, taking into account local sourcing of amendments, regional plant selection, seasonal adaptation;

ii) Collecting a robust dataset at the Open Sky Lab  to train and validate a functional soil-plant model; iii) Developing an AI-based predictive surrogate model to assess critical soil-plant features, including water and nutrient retention capacity, carbon sequestration potential, biomass productivity, biodiversity enhancement, microclimate effects.

• 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 Open Sky Lab  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, specifically those linked to WP4.
• Support outreach activities for WP4, including the dissemination of project results and engagement with stakeholders.
• Present research findings at seminars and meetings organised by the hosting laboratory (Earth & Life Institute).

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Research Field Environmental science » Natural resources management Education Level Master Degree or equivalent

Skills/Qualifications

• Academic background :
  Master’s degree in bioengineering, agricultural engineering, environmental engineering, or a closely related discipline.
• Expertise in soil-plant systems :
  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 and outreach :
  Excellent communication skills, with the ability to engage effectively with academic, and public stakeholders.
• Language proficiency :
  Fluent written and spoken English (additional languages are a plus).

Research Field Environmental…