Orange Business Services

about the role

Your role is to perform a PhD work on «Responsibility chains and their interdependencies in complex systems»

Recent regulatory obligations (NIS2, CRA, AI Act, DORA Act) reinforce the need to know and control the distribution of responsibilities. However, our infrastructures, hardware and software are increasingly the result of the dynamic composition of several independent products provided by different players in different fields and with several levels of delegation. This complicates Orange’s ability to control our supply chains, our commitments to our customers and our new obligations to the new regulations mentioned above.

In the context of our research on the management of responsibilities we have formalized the distribution of responsibilities within such complex systems in the form of graphs. The objective is now to study how to use these graphs to understand and control chains of responsibility, minimize exposure.

The scientific locks to be solved are the following:

1. What lessons can be drawn from different formalisations of the responsibility graph?
2. Can the application of learning tools on these graphs (e.g. GNN) allow us to better characterize/ predict problems of sharing/ propagation of responsibility (dilution, concentration, compliance with commitments, CSR impact/energy cost, exposure to the risk of penalties)
3. Can liability metrics be integrated into management feedback loops (e.g. investment optimization)?

about you

Skills (scientific and technical) and personal qualities required by the position

• Proficient in graphs and graphs analysis
• Proficient in computer development and programming languages (C/C++, python)
• Proficient in data mining and machine learning
• Competent in network architectures (local, INTERNET, 4G, 5G)
• Advanced Beginner or Competent in security
• Capacity for autonomy and synthesis
• Fluency, both written and oral, in English is essential

Education : Master’s degree in Applied Mathematics or Computing and Security

Working Experience

• Internships in computer development using multiple programming languages
• And/Or insternships in the domain of cybersecurity
• A first experience in a Research environment is an advantage

additional information

The proposed subject combines different aspects all falling within innovative technical fields:

• Graph Machine Learning techniques on heterogeneous graphs that evolve over time
• Techniques to establish trustworthy relationships between a device which requires security and security services available at the edge of the network.
• Aspects related to the consideration of energy costs and responsibility chains.

The PhD student will have the opportunity to work directly with our academic, institutional or industrial partners, in an international environment.

department

Orange Innovation brings together the research and innovation activities and expertise of the Group's entities and countries. We work every day to ensure that Orange is recognized as an innovative operator by its customers and we create value for the Group and the Brand in each of our projects. With 720 researchers, thousands of marketers, developers, designers and data analysts, it is the expertise of our 6,000 employees that fuels this ambition every day.

Orange Innovation anticipates technological breakthroughs and supports the Group's countries and entities in making the best technological choices to meet the needs of our consumer and business customers.

Within Innovation, you will be integrated into the CR-BIS management of IT and Services (IT-S), the "Secure Elements & Safe Applications for Mobile Equipment" (SESAME) department brings together about forty engineers, researchers and technicians working in the field of security elements to secure access to networks and services (payment, transport, access control, digital identity) and in the field of securing communications of IoT objects. This entity works closely with the other Group Divisions in charge of security research, development and operations.

contract

Thesis