Data‑Driven and Physically Informed Surrogate Modelling of Soil–Structure Interaction in
Permafrost Conditions
Place: Navier Laboratory, ENPC (77420, Champs-sur-Marne, France)
Duration: 3 years (September 2026 – August 2029)
Salary: ~2350 euros/month (gross salary / salaire brut)
Funding: ANR project PERMACHANGE
Advisors: Lina-María Guayacán-Carrillo, Jean-Michel Pereira and Anh Minh Tang.
Collaborations: Geosciences Environment Toulouse (GET) laboratory
Processes and Engineering in Mechanics and Materials (PIMM) laboratory
Scientific overview:
This PhD thesis aims at studying the impacts of climate change-induced permafrost thaw in the Arctic,
by using advanced thermo-hydro-mechanical (THM) modelling capabilities developed in the
framework of the PERMACHANGE project. Permafrost is soil permanently frozen in depth, covering
a quarter of Northern Hemisphere lands. Due to climate warming, it is experiencing fast and
widespread thawing, and this induces essential impacts in the Arctic, both on the environment (e.g.,
water resources) and on societies (e.g., infrastructure destabilisation). These permafrost thaw impacts
are expected to generate significant additional financial costs for maintaining key human activities,
up to hundreds of billions of dollars by the end of the century. Moreover, permafrost thaw will likely
trigger critical climatic feedback. Thus, anticipating permafrost thaw by numerical simulations is
paramount for ensuring the resilience of Arctic environments, societies and activities while
controlling the associated costs. Meanwhile, numerical simulations of permafrost dynamics are
highly complex and challenging due to the strong non-linearities and couplings involved in the related
physics.
This PhD aims at adding soil mechanics (M) simulation capabilities to the TH hybrid twin. The
detailed objectives are: (1) simulating the effect of temperature change on geotechnical
infrastructures, and (2) building a mechanical surrogate model.
Tasks:
The thesis work is divided into two tasks:
- Finite element simulations of typical geotechnical infrastructures. This task entails the
implementation of numerical modelling across various scenarios to address the effect of
permafrost thawing on typical geotechnical infrastructures (roads, building foundations, slopes,
etc.). Extensive parametric studies will be undertaken to examine the thermal and mechanical
performance under different scenarios, which encompass soil conditions, infrastructure
characteristics, and thermal variations. These parametric studies will critically analyse soilstructure interactions under these diverse conditions. To achieve this objective, numerical thermomechanical simulations utilising a finite element code will be executed. It is important to note that
the experimental results from prior projects will provide substantial insights for the interpretation
and contextualization of new findings. A substantial quantity of numerical data will be produced
from these comprehensive investigations. - Building a mechanical surrogate model. This task aims to propose a straightforward and definitive
tool for expeditious and reliable support. Consequently, in light of the outcomes obtained in the
previous task, this tool will be trained on various scenarios to more effectively consider
uncertainties primarily associated with soil variability in terms of mechanical and thermal
properties. Indeed, recent investigations conducted by the Navier team have demonstrated the
efficacy of employing machine learning approaches to furnish engineers with a rapid
computational asset for structural design and monitoring. Given that numerical models
incorporating multi-physical couplings generally necessitate extensive computational time, the
development of machine learning-based surrogate models will be pursued. The subsequent task
involves formulating a machine learning-based methodology, encompassing data cleaning and
pre-processing, synthetic generation and database creation, culminating in the application of
machine tools. Machine learning-based surrogate models will be developed based on the previous
endeavours of the Navier team (Richa et al. 2024 and Tristani et al. 2024). Finally, based on
symbolic regression approaches, a methodology will be tested to incorporate data from
experimental tests and numerical outcomes to derive simple mathematical expressions for
evaluating soil-structure interaction, ensuring reliable predictions over time (e.g. GuayacanCarrillo et al. 2024).
Needed skills and knowledge:
Numerical modelling (experience in Finite Element Methods and/or AI-based modelling)
Collaborative work in a large and diverse international team
Interest in scientific communication and writing
Although not mandatory, a background in geotechnics would be appreciated. - How to apply:
Please send your CV and cover letter to us HERE
Your application will be evaluated, and if you are shortlisted for an interview, we will contact you.
Application deadline: 31st of March 2026
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