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MASTER'S INTERNSHIP / ENGINEERING FINAL YEAR PROJECT (6 months)

  • On-site
    • Albi, Occitanie, France

Job description

MASTER'S INTERNSHIP / ENGINEERING FINAL YEAR PROJECT (6 months)

Designing Graph Analysis Algorithms for Vulnerability Diagnosis of Interdependent Critical Infrastructures

Applications accepted until February 14, 2026.

Duration: 6 months
Possible dates: Between March and August 2026
Compensation: According to French current regulations
Location: Université Paris 8 (Computer Science Laboratory), Saint-Denis (93) or, alternatively, IMT Mines Albi (Industrial Engineering Center), Albi (81)

This internship is part of the ANR JCJC METRI-KIT project (2026-2030, €302k) led by the CGI (Industrial Engineering Center) at IMT Mines Albi (cgi.mines-albi.fr). Facing climate crises and the growing interdependence of infrastructures (energy, water, healthcare, transportation...), territories need tools to anticipate their vulnerabilities and strengthen their resilience.

The project aims to develop an open-source decision support tool enabling critical infrastructure managers and local authorities to visualize, understand, and diagnose the fragility points of their systems and interdependencies.

You will contribute to addressing one of the project's scientific challenges: designing generic vulnerability identification algorithm(s) based on structural and semantic analysis of knowledge hypergraphs.

# Concrete Objectives:

  1. Model a first case study on interdependent critical infrastructures already known to CGI and their territorial context (water networks, electricity, hospitals, supply chains...) as knowledge hypergraphs.

  2. Following a scientific method, design and implement graph theory-based methods to automatically identify: critical nodes or cascading vulnerabilities, for example. Evaluate the algorithms on the realistic mini case study.

  3. Write an international conference paper (CRITIS, ESREL, ISCRAM or equivalent) with the supervision team.

Funded PhD opportunity: possibility to continue with a PhD (3 years, ANR funding secured) on the same topic after the internship
Societal impact: contribution to territorial resilience facing climate crises
Open-source tool: contribution to R-IOSuite (r-iosuite.com), an open-source research platform used in several French and European projects

# Scientific Team, based at IMT Mines Albi:

  • Prof. Myriam Lamolle – Co-director of CGI, expert in knowledge graphs and ontologies (ORCID)

  • Dr. Cléa Martinez – Assistant Professor, expert in optimization (ORCID)

  • Dr. Clara Le Duff – Assistant Professor, expert in metamodeling and risk management (ORCID)

  • Dr. Audrey Fertier – METRI-KIT Project Coordinator, expert in metamodeling and decision support systems (ORCID)

# Required Profile

Education: Master's degree (M2 or final year of engineering school) in Computer Science, Applied Mathematics, Operations Research, or related field

Essential technical skills:

  • Mastery of classic graph theory concepts and algorithms

  • Ability to conceptualize complex systems

  • Proficiency in graph analysis tools and methods

  • Excellent written comprehension, good written expression (reading/writing articles), and good oral comprehension in English

Expected personal qualities:

  • Ability to propose algorithmic ideas during the interview

  • Proactive in scientific exchanges and idea validation

  • Interest in mathematical formalization and methodical experimentation

  • Appreciation for literature review

Additional assets:

  • Research experience (lab internship, R&D project...)

  • Knowledge of Neo4J and Cypher language

  • Knowledge of ontologies or metamodels

  • Interest in decision support systems

# Application

Application Documents:

  • Detailed CV

  • Cover letter explaining your interest in the subject and your skills

  • M1 and M2 transcripts (or equivalent)

  • Reference contact

Send to:

Email subject: "Application Master's Internship METRI-KIT"

Selection Process: A scientific interview (~45 min) to discuss your background, motivation, skills, and professional project; followed by thinking aloud on an algorithmic problem related to the subject. Response within 1 week.

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