{"id":1004,"date":"2026-02-09T08:48:32","date_gmt":"2026-02-09T08:48:32","guid":{"rendered":"https:\/\/scholarshipidea.net\/?p=1004"},"modified":"2026-02-09T08:48:33","modified_gmt":"2026-02-09T08:48:33","slug":"phd-data-driven-and-physically-informed-surrogate-modelling-of-soil-structure-interaction-in-permafrost-conditions-france","status":"publish","type":"post","link":"https:\/\/scholarshipidea.net\/?p=1004","title":{"rendered":"PhD: Data\u2011Driven and Physically Informed Surrogate Modelling of Soil\u2013Structure Interaction in Permafrost Conditions (France)"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"669\" src=\"https:\/\/scholarshipidea.net\/wp-content\/uploads\/2026\/02\/Navier-Lab.jpeg\" alt=\"\" class=\"wp-image-1005\" srcset=\"https:\/\/scholarshipidea.net\/wp-content\/uploads\/2026\/02\/Navier-Lab.jpeg 800w, https:\/\/scholarshipidea.net\/wp-content\/uploads\/2026\/02\/Navier-Lab-300x251.jpeg 300w, https:\/\/scholarshipidea.net\/wp-content\/uploads\/2026\/02\/Navier-Lab-768x642.jpeg 768w\" sizes=\"auto, (max-width: 800px) 100vw, 800px\" \/><\/figure>\n\n\n\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/scholarshipidea.net\/wp-content\/uploads\/2026\/02\/PhD-thesis-proposal-2026_vf-155334b370c1b869864d744de7a26283.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of PhD-thesis-proposal-2026_vf-155334b370c1b869864d744de7a26283.\"><\/object><a id=\"wp-block-file--media-35a972f5-f162-4134-85fa-b73c2b72e76f\" href=\"https:\/\/scholarshipidea.net\/wp-content\/uploads\/2026\/02\/PhD-thesis-proposal-2026_vf-155334b370c1b869864d744de7a26283.pdf\">PhD-thesis-proposal-2026_vf-155334b370c1b869864d744de7a26283<\/a><a href=\"https:\/\/scholarshipidea.net\/wp-content\/uploads\/2026\/02\/PhD-thesis-proposal-2026_vf-155334b370c1b869864d744de7a26283.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-35a972f5-f162-4134-85fa-b73c2b72e76f\">Download<\/a><\/div>\n\n\n\n<p>Data\u2011Driven and Physically Informed Surrogate Modelling of Soil\u2013Structure Interaction in<br>Permafrost Conditions<br>Place: Navier Laboratory, ENPC (77420, Champs-sur-Marne, France)<br>Duration: 3 years (September 2026 \u2013 August 2029)<br>Salary: ~2350 euros\/month (gross salary \/ salaire brut)<br>Funding: ANR project PERMACHANGE<br>Advisors: Lina-Mar\u00eda Guayac\u00e1n-Carrillo, Jean-Michel Pereira and Anh Minh Tang.<br>Collaborations: Geosciences Environment Toulouse (GET) laboratory<br>Processes and Engineering in Mechanics and Materials (PIMM) laboratory<\/p>\n\n\n\n<p>Scientific overview:<br>This PhD thesis aims at studying the impacts of climate change-induced permafrost thaw in the Arctic,<br>by using advanced thermo-hydro-mechanical (THM) modelling capabilities developed in the<br>framework of the PERMACHANGE project. Permafrost is soil permanently frozen in depth, covering<br>a quarter of Northern Hemisphere lands. Due to climate warming, it is experiencing fast and<br>widespread thawing, and this induces essential impacts in the Arctic, both on the environment (e.g.,<br>water resources) and on societies (e.g., infrastructure destabilisation). These permafrost thaw impacts<br>are expected to generate significant additional financial costs for maintaining key human activities,<br>up to hundreds of billions of dollars by the end of the century. Moreover, permafrost thaw will likely<br>trigger critical climatic feedback. Thus, anticipating permafrost thaw by numerical simulations is<br>paramount for ensuring the resilience of Arctic environments, societies and activities while<br>controlling the associated costs. Meanwhile, numerical simulations of permafrost dynamics are<br>highly complex and challenging due to the strong non-linearities and couplings involved in the related<br>physics.<br><br>This PhD aims at adding soil mechanics (M) simulation capabilities to the TH hybrid twin. The<br>detailed objectives are: (1) simulating the effect of temperature change on geotechnical<br>infrastructures, and (2) building a mechanical surrogate model.<br>Tasks:<br>The thesis work is divided into two tasks:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Finite element simulations of typical geotechnical infrastructures. This task entails the<br>implementation of numerical modelling across various scenarios to address the effect of<br>permafrost thawing on typical geotechnical infrastructures (roads, building foundations, slopes,<br>etc.). Extensive parametric studies will be undertaken to examine the thermal and mechanical<br>performance under different scenarios, which encompass soil conditions, infrastructure<br>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<br>the experimental results from prior projects will provide substantial insights for the interpretation<br>and contextualization of new findings. A substantial quantity of numerical data will be produced<br>from these comprehensive investigations.<\/li>\n\n\n\n<li>Building a mechanical surrogate model. This task aims to propose a straightforward and definitive<br>tool for expeditious and reliable support. Consequently, in light of the outcomes obtained in the<br>previous task, this tool will be trained on various scenarios to more effectively consider<br>uncertainties primarily associated with soil variability in terms of mechanical and thermal<br>properties. Indeed, recent investigations conducted by the Navier team have demonstrated the<br>efficacy of employing machine learning approaches to furnish engineers with a rapid<br>computational asset for structural design and monitoring. Given that numerical models<br>incorporating multi-physical couplings generally necessitate extensive computational time, the<br>development of machine learning-based surrogate models will be pursued. The subsequent task<br>involves formulating a machine learning-based methodology, encompassing data cleaning and<br>pre-processing, synthetic generation and database creation, culminating in the application of<br>machine tools. Machine learning-based surrogate models will be developed based on the previous<br>endeavours of the Navier team (Richa et al. 2024 and Tristani et al. 2024). Finally, based on<br>symbolic regression approaches, a methodology will be tested to incorporate data from<br>experimental tests and numerical outcomes to derive simple mathematical expressions for<br>evaluating soil-structure interaction, ensuring reliable predictions over time (e.g. GuayacanCarrillo et al. 2024).<br><br>Needed skills and knowledge:<br>Numerical modelling (experience in Finite Element Methods and\/or AI-based modelling)<br>Collaborative work in a large and diverse international team<br>Interest in scientific communication and writing<br>Although not mandatory, a background in geotechnics would be appreciated.<br><\/li>\n\n\n\n<li>How to apply:<br>Please send your CV and cover letter to us HERE<br>Your application will be evaluated, and if you are shortlisted for an interview, we will contact you.<br><strong>Application deadline: 31st of March 2026<\/strong><\/li>\n<\/ol>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data\u2011Driven and Physically Informed Surrogate Modelling of Soil\u2013Structure Interaction inPermafrost ConditionsPlace: Navier Laboratory, ENPC (77420, Champs-sur-Marne, France)Duration: 3<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[12,13,8],"class_list":["post-1004","post","type-post","status-publish","format-standard","hentry","category-scholarship","tag-civil-engineering","tag-france","tag-phd"],"_links":{"self":[{"href":"https:\/\/scholarshipidea.net\/index.php?rest_route=\/wp\/v2\/posts\/1004","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scholarshipidea.net\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/scholarshipidea.net\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/scholarshipidea.net\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/scholarshipidea.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1004"}],"version-history":[{"count":1,"href":"https:\/\/scholarshipidea.net\/index.php?rest_route=\/wp\/v2\/posts\/1004\/revisions"}],"predecessor-version":[{"id":1007,"href":"https:\/\/scholarshipidea.net\/index.php?rest_route=\/wp\/v2\/posts\/1004\/revisions\/1007"}],"wp:attachment":[{"href":"https:\/\/scholarshipidea.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1004"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/scholarshipidea.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1004"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/scholarshipidea.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1004"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}