Postdoc in Ontology Engineering

**Postdoc in Ontology Engineering**: - Réf **ABG-133686** - Stage master 2 / Ingénieur - Durée 12 mois - 04/10/2025 - Centre de Recherche en Automatique de Nancy ( CRAN ) - Lieu de travail- Nancy and Epinal Grand Est France - Champs scientifiques- Informatique - Mots clés- Ontology engineering, Ethical risks, Ethical factors- 15/11/2025 **Établissement recruteur**: Created in 1980, the CRAN is a "Mixed Research Unit - UMR 7039" shared by the University of Lorraine (Scientific Pole "Automation, Mathematics, Computer Science and their Interactions - AM2I") and the CNRS (Institute "CNRS Computer Sciences"). The laboratory has been classified as a restricted zone since February 2014. It is spread over 8 geographical sites. The laboratory has a total of nearly 250 members: as of January 1, 2024, there are 120 researchers or teacher-researchers (including 8 CNRS researchers - section 7 of the CoNRS), researchers from the Lorraine Cancer Institute (ICL), the Regional University Hospital Center (CHRU) or external organizations, 5 emeritus researchers. The administrative department and the research support department have a total of 27 staff members. CRAN welcomes nearly one hundred doctoral students, postdoctoral fellows, and visiting researchers. In 2023, its scientific output included nearly 300 articles or papers in national and international journals and conferences. CRAN's work relies on around twenty pieces of equipment (prototypes, demonstrators, platforms, some of which are open and certified) and develops software and decision-making tools. **Description**: **Context**: - The ANR JCJC DET project seeks to propose innovative methodologies to bridge the gap between ethics and automation in smart manufacturing. The project will contribute to modeling, developing, and engineering digital ethical twins (DETs) - digital twins capable of conducting ethical reasoning. Ethical factors are a prerequisite for ethical reasoning. This work will primarily focus on ethical factor identification within the context of smart manufacturing. **Problem Statement**: - Existing approaches to ethical factor identification are frequently ad hoc, relying on manual interpretation of guidelines or fragmented domain expertise [1]. Such methods hinder scalability, interoperability, and traceability, making it difficult to ensure that ethical considerations are systematically embedded across the full lifecycle of a system. The ethics-related information covered in standards such as Responsabilité Sociétale de l’Entreprise (RSE) [2], Règlement Général sur la Protection des Données (RGPD) [3] remains vague and incomplete. For example, RSE addresses only economical, social and environmental aspects. RGPD pertains exclusively to the European Union and focuses solely on data protection matters. The standards also often evolve less rapidly than technologies [4]. Without a structured, machine-interpretable representation of ethical knowledge, practitioners and researchers face significant barriers in automating ethical assessment, aligning interdisciplinary perspectives, and adapting to evolving regulatory landscapes. **Tasks**: - T1 : Literature review on ethical factors in smart manufacturing - Conducting a systematic literature review ; - Describing the identified ethical factors and their relationships ; - Performing the classification of ethical factors. - T2 : Identifying ethical factors in real scenarios - Visiting relevant smart manufacturing factories to build ethical risk scenarios ; - For each scenario, - describing operational activities; - describing planification activities ; - identifying and structuring the cnstraints; - evaluating the ethical factors with experts. - T3 : Developing an ontology-based ethical risk scenario knowledge base - Proposing a methodology based on the ontology development guide to provide a step-by-step approach ; - Designing a meta-model explaining the major classes and the relationships among them ; - Building the class taxonomies of different facts of ethical risk knowledge for risk identification ; - Defining properties and relations for classes ; - Defining Semantic Web Rule Language (SWRL) rules to enable ontology reasoning **References**: - [1] Dindler, C., Krogh, P. G., Tikær, K., & Nørregård, P. S. (2022). Engagements and articulations of ethics in design practice. _International Journal of Design_, _16_(2), 47-56. - [2] Igalens, J., & Gond, J. P. (2020). _La responsabilité sociale de l'entreprise_. QUE SAIS-JE. - [3] Desgens-Pasanau, G. (2019). Le cadre législatif et réglementaire du Règlement général sur la protection des données (RGPD). _I2D-Information, données & documents_, _1_(1), 12-20. - [4] Guenduez, A. A., Walker, N., & Demircioglu, M. A. (2025). Digital ethics: Global trends and divergent paths. _Government Information Quarterly_, _42_(3), 102050. **Profil**: - Ph.D. in Industrial Engineering, **Computer Science**,**Information Systems**,**Knowledge Engineering**,**Artific