The offered position is proposed by the RESIST team of the Inria Nancy Grand Est research lab, the French national public institute dedicated to research in digital Science and technology. The team is one of the European research group in network management and is particularly focused on empowering scalability and security of networked systems through a strong coupling between monitoring, analytics and network orchestration.

https://team.inria.fr/resist/

This work is in the context of the H2020 AI@EDGE project (A Secure and Reusable Artificial Intelligence Platform for Edge Computing in Beyond 5G Networksstarts in January, 2020) involving partners all around Europe.

This work is in the context of the H2020 AI@EDGE project (A Secure and Reusable Artificial Intelligence Platform for Edge Computing in Beyond 5G Networksstarts in January, 2020) involving partners all around Europe. This project will bring different use cases for testing solutions. It will define a framework and techniques to empower the automation of network security assuming a highly dynamic environment, in particular Mobile-Edge Computing infrastructure and 5G. In such an environment, resources are virtualized and shared among users with an underlying high volatility and changes in resource allocations and needs.

In this project, the main contributions of the RESIST team is to automate security mitigation techniques using reinforcement learning and automatically generate datasets for evaluating AI algorithms in different configurations.

The engineer will be integrated in the RESIST research team to support the realization of the highlighted contributions including support for research with the development of tools and software, identification and tests of AI algorithms, project documentation and task management.

The engineer will be integrated in the RESIST research team to support the realization of the project objectives including the following tasks:
• The identification of components to support automationof AI-based services and dataset generation in the overall project architecture,
• Support the development and integration of the automation solutions which is the main topic of a PhD student working in this project,
• Actively participate to the research work regarding automated dataset generation and the development of related tools. The automated generation will be based on initial datasets to be augmented using AI techniques,
• Integration and validation of our developed tools in project use cases
• Participate to standardization activities by presenting our work in different fora
• Project management: the engineer will be fully involved in the project management duties including writing, reviewing and editing of deliverables of the project; participating to the project meetings, including physical meetings being held in different countries of Europe and review meetings; management of tasks and a workpackage involving multiple partners.

• Required qualification: Diplôme d’ingénieur, Master degree in Computer Science or Computer engineering
• Required knowledge: networking, network security
• Languages: Shell, python and others are appreciated
• Software developement: continuous integration and collaborative development using gitlab, knowledge in virtualisation technologies (Docker, Kubernetes)
• Knowledge in machine learning and data mining
• Fluent in engish (writing and oral communication)
• Comfortable with leading meetings, webconferences, project management

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

Gross salary : from 2 562 euros before deduction of tax incomes depending on laboral experiences and degrees.