Thèse Reach Resilient Equitable Access To Care After Hazards H/F - Doctorat.Gouv.Fr

Établissement : IMT Mines Albi École doctorale : SYSTEMES Laboratoire de recherche : CGI - Centre de Génie Industriel Direction de la thèse : Safa LAYEB ORCID 0000000325367872 Début de la thèse : 2027-09-01 Date limite de candidature : 2026-11-23T23:59:59 Climate change is increasing the frequency and intensity of natural hazards such as heatwaves, floods, wildfires and storms. Beyond their direct effects on health, such events can disrupt the infrastructures, workforce, supply chains, transport and communication systems required to maintain healthcare delivery. They can therefore reduce healthcare capacity precisely when demand is increasing. These disruptions may be particularly critical in rural, remote and underserved territories that already face structural difficulties in accessing care.

The REACH project (Resilient Equitable Access to Care after Hazards) aims to investigate how healthcare networks can be designed and managed to maintain resilient and equitable access to care when healthcare demand, facility and workforce availability, and transport conditions are uncertain and evolve rapidly over time and space.

The methodology will primarily rely on stochastic and robust optimization models to represent a range of disruption conditions and identify strategies that perform well across different scenarios. The project will investigate strategies such as resource allocation, coordination of patient diversion and inter-hospital transfers, and deployment of temporary or mobile healthcare capacity. Equity in access to care will be explicitly incorporated through indicators such as geographical coverage, travel and waiting times, and unmet demand. Where necessary, simulation approaches will complement optimization models to represent congestion and rapidly evolving post-disaster conditions.

The project will adopt a dynamic and multi-scale perspective and investigate two complementary contexts, in France and Türkiye. The objective is not to develop two separate models, but to build a generic and adaptable framework for resilient and equitable healthcare network design under uncertainty. The two case studies will make it possible to identify common mechanisms and transferable strategies while accounting for differences in healthcare organization and hazard characteristics.

REACH will contribute to the development of more robust, adaptive and equitable preparedness and response strategies. The results will provide quantitative tools and recommendations for healthcare planners, health authorities and emergency-management actors, supporting the resilience of healthcare systems while avoiding the amplification of existing territorial inequalities in access to care. Natural hazards can simultaneously increase healthcare needs and disrupt healthcare capacity by affecting facilities, healthcare workers, transport and supply chains. These effects are particularly critical in rural, remote and underserved territories, where access to care is already uneven.

Healthcare network design has been widely studied through Operations Research, particularly facility location, capacity planning and patient allocation models. Existing work has also addressed healthcare networks under uncertainty and the deployment of temporary or mobile facilities following disasters. However, these approaches generally focus on specific aspects of network resilience, while the combined consideration of uncertainty, dynamic disruptions and equity of access remains to be further explored.

REACH builds on this research by developing stochastic and robust optimization models that integrate these dimensions. The project will specifically investigate how healthcare networks can maintain equitable access to care while adapting to rapidly changing demand and capacity, with particular attention to vulnerable territories and primary care access.

We are looking for a candidate holding a Master's degree, an Engineering degree, or an equivalent qualification, with a specialization in Operations Research, Combinatorial Optimization, Computer Science, or a related field.

Expected skills:

*Ability to formulate complex problems (LP/MILP/...) and to identify and apply appropriate optimization methods to solve them.
* Strong skills in algorithms and programming (C++, Python, or Java);
* Ability to formulate and solve complex problems;
* Good written communication skills in both French and English for documentation and dissemination of research results;
* An interest in healthcare, crisis management, or territorial planning would be an asset.
* Skills in simulation would be an asset

Eligibility : Applicants must not have lived, worked, or carried out their studies in France for more than 12 months during the 36 months immediately preceding the application deadline.

Application link : https://edd-projets.utoulouse.fr/

• Python
• C++
• Anglais
• Java
• Français