PhD opportunity in international cotutelle UPPA–EHU

We are looking for an excellent and highly motivated candidate to prepare a joint PhD proposal within the 2026 UPPA–EHU call for cotutelles de thèses. Because the UPPA–EHU cotutelle call will select projects mainly on the basis of the academic excellence of the PhD candidate, applicants should have an outstanding academic record.

The PhD project will be embedded in a new interdisciplinary research project aimed at developing next-generation tools for monitoring and predicting forest health under climate change. Forests are increasingly exposed to drought, heat waves and other stressors, yet current monitoring systems often rely on late and coarse indicators of decline. ColorLink addresses this challenge by using canopy colour as an early diagnostic signal, linking RGB-derived vegetation indices with tree physiology, growth, microclimate, soil conditions and ecosystem-service indicators.

The PhD thesis will focus on the integration, harmonisation, modelling and prediction of multi-scale forest-health datasets. The main objective will be to develop predictive models capable of forecasting tree health transitions and ecophysiological functioning by combining: leaf-level biomarkers and physiological traits; dendrometer and sap-flow data; soil microclimate sensor information; RGB imagery and colour-derived vegetation indices and forest-condition indicators. The project will involve the construction of a harmonised database and the development of statistical and machine-learning models for early warning, nowcasting and forecasting of forest functioning. The thesis will therefore sit at the
interface between forest ecophysiology, environmental monitoring, data science and ecological modelling.

Candidate profile
We are especially interested in candidates with one of the following profiles:
1. a strong background in biology, plant science, ecology, forestry, environmental sciences or ecophysiology, combined with a clear enthusiasm for quantitative analysis, modelling, programming and mathematics; or
2. a strong background in mathematics, statistics, data science, computer science, physics or engineering, combined with a genuine interest in plant functioning and climate-change impacts.
Previous experience with R, statistics, plant physiology or ecological modelling will be positively valued, but motivation, intellectual curiosity and the ability to work across disciplines are equally important.

Research environment
The selected candidate will join a highly interdisciplinary and collaborative research environment at the University of the Basque Country (EHU), within the BEZEKOFISKO research group, and will work in close collaboration with the UPPA team as part of the cotutelle framework. At UPPA the project will be supported by the Probability and Statistics team from the laboratory of applied mathematics and its application (LMAP). This team will provide complementary expertise in data science, statistics, machine learning and deep learning. At EHU, the project is supported by strong expertise in plant stress ecophysiology, ecosystem services, soil–plant interactions, environmental monitoring, and RGB image analysis. The
team has access to advanced laboratory and field.

Training and career development
The PhD will provide advanced training in: forest and plant ecophysiology; environmental sensor networks and high-frequency monitoring; image-derived vegetation indices; data cleaning, harmonisation and FAIR data principles; statistical modelling and machine learning; scientific writing, conference communication and outreach. The candidate will develop an Individual Development Plan and will be encouraged to lead scientific publications arising from the thesis. Training will also include participation in doctoral courses, research
meetings, national and international conferences, and outreach activities.

Depending on the final cotutelle structure, research stays between the participating institutions will be planned as part of the doctoral training. The PhD candidate will benefit from an intellectually stimulating environment, with opportunities to interact with international collaborators, stakeholders and technical partners.

APPLICATION PROCEDURES
Interested candidates should send an expression of interest to raquel.esteban@ehu.eus and benoit.liquet@univ-pau.fr by 17 June 2026, including: a CV; academic transcripts, including grades and ranking if available; a short motivation letter explaining their interest in the project and their fit with the profile.

This is an excellent opportunity for a candidate who wants to build a PhD at the frontier between forest ecology, plant physiology, environmental sensing and artificial intelligence, contributing to the development of innovative tools for climate-smart forest monitoring and management.