Systems Engineering
Work description
The research fellowship is situated at the intersection of human-centred Artificial Intelligence, production management, and AI systems governance. Its objective is to investigate how the lifecycle of AI systems, from data preparation to model use, monitoring, and updating, should be aligned with human activities, organisational processes, and decision-making in industrial contexts. The fellowship aims to contribute to the development of integration models that explicitly define the roles of different actors, the points requiring human oversight, the mechanisms for collaboration between workers and AI systems, and the requirements related to trust, explainability, accountability, and intervention capabilities. Different production management contexts will be considered, including planning, operations, maintenance, and quality control. Specifically, the main activities to be carried out by the fellowship holder are: • Analyse the state of the art in AI lifecycles, MLOps, Human-Centred AI, AI governance, and organisational integration in industrial contexts; • Identify and model, notably using BPMN 2.0, the activities, actors, decisions, and organisational structures associated with the AI lifecycle in production management; • Support elicitation and validation sessions with experts and industrial stakeholders, refining the models developed; • Identify the integration points between AI systems and production routines and propose policies for accountability, escalation, auditability, oversight, and compliance; • Develop maps of interdependencies between workers and AI systems, including task allocation modes, handover and fallback mechanisms, and responsibility assignment; • Investigate policies for the documentation, versioning, access, sharing, updating, monitoring, and retirement of AI models; • Translate the results into functional and non-functional requirements for the project’s technological platform, including requirements for logging, traceability, monitoring, and control; • Collaborate in the preparation of reports, methodological documentation, scientific articles, and other technical and scientific project outputs. The training plan will combine literature review, conceptual and process modelling, analysis of industrial cases, stakeholder engagement, and iterative validation of the artefacts produced, contributing to the responsible and human-centred integration of Artificial Intelligence in production management.
Academic Qualifications
Master's degree in Computer Engineering, Information Systems, or a related field
Minimum profile required
Master’s degree final grade above 14.
Preference factors
- Knowledge of AI governance and the lifecycle of AI systems - Experience in the analysis, specification, or modelling of organisational processes.
Application Period
Since 23 Jul 2026 to 05 Aug 2026
Centre
Industrial & Systems Engineering and Management