Postdoctoral position : semantic analysis of action traces for competences identification and capitalization from industrial practices

INSA Lyon is France's leading post-baccalaureate engineering school. With its 22 laboratories, INSA Lyon develops a mutlidisciplinary research policy together with partners from its close environment from Lyon region.

Within INSA Lyon, the recruited person will be hosted in the DISP laboratory. This laboratory gathers researchers around a double expertise in Industrial Engineering and Information technology for business. To meet the scientific challenges imposed by the ongoing changes within the socio-economic world, DISP research activities deal with the design and deployment of decision-making methods and information systems in order to improve the performance, the agility and the resilience of goods and services production systems and comprehensive supply chains. Its double expertise, based on competencies in modelling, operational research, simulation, software engineering, artificial intelligence, planning, scheduling, and decision-making, allows studying these complex systems by addressing simultaneously their technical, structural, organizational and human-related dimensions. The team supervising this postdoc is particularly specialized in knowledge management and business process intelligence.

Context:

In an industrial context characterized by a high complexity in business processes and proliferation of information systems, the company's actors implement many learnings and practices to overcome this complexity. These learnings and practices encompass implicit and explicit competences that we aim to extract, manage and connect to business processes and products/services to enhance the company’s competences and capitalize their reuse.

The CaTCaP project, funded by ANR (french national research agency), is a multidisciplinary project in knowledge engineering that aims to extract the competences encompassed in current practices and learnings in order to manage the intellectual capital of a company by developing and/or by changing the existing competences and intelligently mobilizing the actors’ competences by defining suitable practices to real competences.

It is about proposing methods allowing to:

• observe and identify learning activities and situations: we propose a mixed approach characterized by the collection and analysis of traces resulting from human observation and activity analysis (video recordings, questionnaires, etc..) as well as operational traces existing in computer systems (log files, modeled traces, etc.).
• extract competences from the analysis of operational traces: once the traces are collected, we identify the relevant links between these traces and competences, identified individually and collectively as relevant to carry out the project activities at Energy Pool. Synergies between these competences are also considered. 
• formalize and represent identified competences considering their nature (soft or hard) and dimension (individual or collective). Respect the personal data confidentiality is considered during this phase. We represent the competences as a mapping that evolves dynamically and can be reconfigurable according to the exploitation’s objectives. 
• identify and evaluate practices from the peers’ point of view: in parallel with observations, interviews with project’ members will make it possible to confront the observed people with the traces of their activities in order to reach the sense that they associate with their practices.
• identify and deploy key success factors during teams’ definition: we develop a correlation analysis between competences, resources, traces, and practices’ evaluations in order to identify key factors that led to different performances.

Contract duration and remuneration:

The employment contract will be for 10 months, starting as soon as possible, but not later than September 1st, 2023

Gross monthly salary of 2725€.

• Relevant skills in text mining, Natural language processing (NLP), knowledge graphs, and semantic reasoning.
• Experience in machine learning will be appreciated.
• Good programming skills (e.g. Python).
• Project Management and teamwork skills are highly appreciated for effective collaboration with the industrial partner involved in the project.