Hybrid Artificial Intelligence Approaches for the Automatic Alignment of Competency Frameworks with Labour Market Needs

Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Project Background: The rapid evolution of occupations, driven by digital transformation, the emergence of generative artificial intelligence, and ecological transitions, is challenging the alignment between the competencies developed in higher education and those expected by employers. Competency frameworks, which are at the core of the Competency-Based Approach (CBA), struggle to keep pace with these changes and quickly become outdated. This interdisciplinary research project, at the intersection of computer science and educational sciences, aims to develop innovative methods for automatically analysing, comparing, and aligning educational competency frameworks with the competencies actually required in professional environments. The project leverages competency frameworks, ontologies, knowledge graphs, occupational taxonomies, and job advertisement corpora to generate reliable, explainable, and adaptive recommendations for curriculum development.

PhD Objectives: The primary objective of this PhD project is to design a hybrid approach combining generative artificial intelligence, machine learning, and ontologies to automatically align heterogeneous competency frameworks. The research will focus on:

• Formal modelling of competencies and educational competency frameworks;
• Integration and interoperability of competency frameworks from multiple sources (ESCO, RNCP, ROME, university curricula, and job advertisements);
• Development of semantic alignment methods using knowledge graphs, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) techniques;
• Design of explainable AI models ensuring the traceability and transparency of the generated recommendations;
• Identification of emerging skills and dynamic analysis of the gap between competencies taught in higher education and those required by socio-economic stakeholders;
• Development of decision-support tools to assist academic programme coordinators in updating competency frameworks.

Duration: 36-month doctoral contract (2026–2029)

Location: University of Montpellier, France

The PhD candidate will be jointly affiliated with:

• LIRMM (Laboratory of Computer Science, Robotics and Microelectronics of Montpellier);
• LIRDEF (Interdisciplinary Research Laboratory in Didactics, Education and Training).

Required Degree: Master's degree (Master 2 or equivalent) in one of the following fields: Computer Science, Artificial Intelligence (including Generative AI)

Keywords: Machine Learning, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Recommender Systems, Competency Frameworks

Supervision and Contact:

Chrysta Pélissier, Associate Professor (HDR), LIRDEF, Specialist in Technology-Enhanced Learning (TEL), Competency-Based Education, and Educational Sciences, Email: chrysta.pelissier@umontpellier.fr

Sandra Bringay and Jérôme Azé, Full Professors, LIRMM, Specialists in Artificial Intelligence, Knowledge Graphs, and Text Data Mining, Email: bringay@lirmm.fr, aze@lirmm.fr

Where to apply

E-mail: bringay@lirmm.fr

Requirements

Research Field: Computer science » Informatics

Education Level: Master Degree or equivalent

Skills/Qualifications

Candidate Profile: Applicants should hold a Master's degree (or an equivalent qualification) in Computer Science, Artificial Intelligence, Data Science, or a closely related field.

Experience in several of the following areas will be considered an asset:

• Artificial Intelligence and Machine Learning;
• Natural Language Processing (NLP);
• Large Language Models (LLMs) and Generative AI;
• Retrieval-Augmented Generation (RAG);
• Ontologies and Knowledge Representation;
• Text Mining and Data Analytics;
• Python programming and AI libraries;
• English proficiency at B2 level or higher.

Specific Requirements

An interest in interdisciplinary research, collaboration with academic and industrial partners, and issues related to higher education, competency-based education, and employability will also be highly valued.

Languages: ENGLISH

Level: Good

Additional Information

Selection process

Application Procedure: Applications must include:

• A curriculum vitae (CV);
• A cover letter;
• A copy of the required degree for doctoral enrolment or, if the degree has not yet been awarded, the most recent Master's transcripts (Semester 1 and/or Semester 2).

Work Location(s)

Number of offers available: 1

Company/Institute: LIRMM

Country: France

City: Montpellier

Postal Code: 34090

Street: LIRMM

Contact

City: Montpellier

Website: http://www.lirmm.fr/

Street: 161 rue Ada

Postal Code: 34095

E-Mail: bringay@lirmm.fr, aze@lirmm.fr, chrysta.pelissier@umontpellier.fr