Socio-climatic modelling and qualitative reasoning for coastal adaptation along the southern coastline of Martinique

The recruited person will be hosted within UMR 8053 PHEEAC — Powers, History, Slaveries, Atlantic Caribbean Environment —, a joint unit of CNRS and the University of the Antilles, on the Schœlcher campus in Martinique. The thesis will be undertaken at the University of the Antilles, within the DEECA doctoral school, in the discipline of “Applied Mathematics and Social Sciences”.

The thesis will be supervised by Jean-Raphaël Gros-Désormeaux, Research Director at CNRS. The supervisory environment will bring together expertise in the humanities and social sciences, anthropology, climate modelling and computer science, with Patricia Cadule, Philippe Hunel and Dimitri Béchacq.

The research will draw on the Martinique HALL 4 demonstration site and its partners: PHEEAC, the Espace Sud Martinique Agglomeration Community, Cerema, BRGM and NBE. The recruited person will benefit from the unit's documentary, computing and software resources, as well as the computing facilities mobilised within the framework of the project, according to the arrangements defined by the consortium.

Working environment — Objective

The objective of the doctoral contract is to design, implement and validate an interdisciplinary approach enabling the exploration of adaptation pathways for the southern coastline of Martinique by linking biophysical vulnerability with social and territorial vulnerability.

The work must contribute to the production of original scientific knowledge and to the development of tools enabling local stakeholders to discuss adaptation scenarios, their uncertainties and their social and environmental implications.

Working environment — Description of activities

The recruited person will be expected to:

• Conduct a review of the state of the art in socio-climatic modelling, coastal digital twins, adaptation governance and qualitative reasoning assisted by artificial intelligence.
• Compile and analyse a corpus of interviews, verbatim transcripts of participatory workshops, and institutional, regulatory and territorial documents.
• Participate in field investigations and workshops with coastal stakeholders, particularly fishers and users, to document local knowledge, ecological memory and perceptions of change.
• Implement qualitative coding, fuzzy cognitive mapping and participatory mapping methods to formalise and spatialise social dynamics.
• Develop and evaluate climate downscaling methods based on global projections, analysing the propagation of uncertainties.
• Design a spatially explicit social model and a qualitative reasoning engine using open language models, a local documentary corpus and model adaptation methods.
• Contribute to coupling these components with DIGI-ADAPT and evaluate their robustness, explainability and relevance in relation to field observations.
• Present the results back to partners and participants, produce scientific publications and presentations, and write and defend the thesis.

Working environment — Required skills

Applicants must hold a master's degree, or an equivalent qualification permitting enrolment in a doctoral programme, in applied mathematics, modelling, computer science, data science or a related discipline involving solid quantitative training.

The expected skills are as follows:

• Solid knowledge of mathematical modelling, statistics and scientific computing.
• Proficiency in a scientific programming language, particularly Python, and the ability to develop reproducible processing procedures.
• Knowledge of machine learning and an interest in language models.
• Ability to analyse complex data and assess the uncertainties and limitations of models.
• Strong interest in the humanities and social sciences, climate issues and island territories.
• Ability to work across disciplines, listen and engage with scientific and territorial partners.
• Autonomy, rigour, organisational abilities and good writing skills.
• Proficiency in French sufficient to conduct interviews and undertake scientific writing; English sufficient for reading, writing and exchanges within an international consortium.

Knowledge of geographic information systems, qualitative analysis, natural language processing or climate modelling will be appreciated. Methods specific to the project may be developed further as part of the doctoral training.

The thesis, entitled “Socio-climatic modelling and qualitative reasoning for coastal adaptation along the southern coastline of Martinique”, aims to develop a modelling framework combining coastal biophysical risks and the social dynamics that condition adaptation to climate change.

The southern coastline of Martinique concentrates challenges related to erosion, coastal flooding and the degradation of coastal ecosystems, in a territory where inhabited areas, tourism activities and fishing activities coexist. Adaptation strategies must take into account climate hazards, the protective functions of ecosystems, coastal uses, relationships with the territory and the conditions under which populations make the measures their own.

The work will focus on three complementary scientific objectives:

• Produce climate projections adapted to the local scale, by comparing physical and statistical downscaling approaches and making uncertainties explicit.
• Characterise and formalise the social dynamics of adaptation: conflicts of use, institutional trust, power relations, environmental justice and the articulation of management responsibilities between land and sea.
• Develop and evaluate a system coupling social modelling and qualitative reasoning assisted by artificial intelligence, intended to be integrated into the DIGI-ADAPT digital twin platform.

The research will pay particular attention to the role of coral reefs in wave attenuation, the traceability of the transition from qualitative data to models, and the presentation of results back to territorial stakeholders. Knowledge derived from fieldwork and simulation results will undergo complementary validation, documenting their convergences and discrepancies.