PhD Student in Computer Science (M/F) - Augmentation and Generation of Geopolymer Data

Job Description

Thesis Subject

The GEOLIANT project (France 2030) aims to develop an artificial intelligence tool to accelerate the design of geopolymer formulations, by predicting their technical properties (porosity, mechanical strength, durability), environmental properties (life-cycle assessment) and economic properties (cost, viability), and by bidirectionally identifying candidate formulations from desired target properties.

The dataset assembled within the project — around 1,600 formulations drawn from the literature (Gabriel Sanson's database, LMDC/INSA Toulouse), enriched through automatic extraction of new articles and through partners' experimental data — has heterogeneous and patchy coverage: not all formulations have the same characterizations available (mechanical strength, shrinkage, sulfate resistance, XRD, SEM, etc.), and the volume of usable data remains limited relative to the diversity of the composition space (precursors, Na₂O/SiO₂ ratios, curing conditions).

The goal of the thesis is to develop data augmentation and generation methods to address these gaps: imputing missing values by exploiting correlations between properties and the physico-chemical proximity between formulations; generating plausible synthetic data (for example via generative models conditioned on composition) to extend coverage of the formulation space without multiplying costly experimental trials; and weighting data confidence according to its provenance (partner data, literature, or generated data), a traceability principle already adopted by the project.

The PhD candidate will build on the property-graph datalake under construction and will work closely with the other PhD candidate on the project, with the augmented/generated data feeding directly into the causal and predictive models targeted by GEOLIANT. The work will combine machine learning (generative models, graph learning), management of heterogeneous and incomplete data, and a strong interdisciplinary dimension with the materials science experts of the consortium (LMDC), needed to ensure the physico-chemical plausibility of the data produced.

Your Work Environment

The thesis is part of the GEOLIANT project, supported by BPI France under the France 2030 program, which aims to develop and industrialize low-carbon geopolymer binders from construction waste, as a sustainable alternative to traditional cement, a heavy CO₂ emitter. The project rests on the development of innovative formulations and on the establishment of digital and predictive tools based on artificial intelligence, in order to accelerate research, evaluation, and performance validation.

The PhD candidate will be affiliated with the LIRIS laboratory and will work within an academic and industrial consortium bringing together stakeholders from civil engineering, materials science, and the environment. The thesis work will take place in a multidisciplinary environment, at the interface between computer science (data extraction and integration, graphs, AI) and materials engineering.

The thesis will be carried out in close interaction with the project's partners, in a setting that fosters co-design, expert validation, and the transfer of results toward operational applications, particularly in the context of pilot construction sites. It offers a stimulating research environment, combining methodological contributions with industrial and environmental impact.

The position falls within a sector subject to the protection of scientific and technical potential (PPST — protection du potentiel scientifique et technique), and therefore requires, in accordance with regulations, that your arrival be authorized by the competent authority of the French Ministry of Higher Education and Research (MESR).

Constraints and risks

Dependence on the quality and availability of data consolidated elsewhere in the project (partner data, extracted literature), whose heterogeneity and patchy nature directly condition the choice and performance of the augmentation and generation methods.

Risk inherent to generative methods: no a priori guarantee of physico-chemical plausibility for the synthetic data produced, requiring iterative validation with the consortium's materials science experts.

Difficulty in evaluating the quality of generated data in a context of limited reference data volume, which may require evolving methodological and technical choices (imputation, generative models, confidence weighting).

Need for a balance between AI-based automation and human validation, which may lengthen certain experimental phases.

Constraints linked to the contractual milestones and deliverables of the project funded by BPI France / France 2030.

Interdisciplinarity of the subject, requiring time to become familiar with concepts in materials science and civil engineering.

Occasional travel possible for meetings, workshops, or validation sessions at partner sites.

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

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