Missions

The role involves designing, developing, and assessing artificial intelligence systems focused on automatically detecting vulnerabilities and malware in software. It is part of the BVSec project, which aims to enhance the security of IT systems and foster scientific innovation in cybersecurity.

Activités

- Design and develop artificial intelligence systems to analyze the security of software systems.
- Identify, classify, and monitor vulnerabilities and malware in code.
- Prepare, structure, and analyze datasets for training and evaluating models.
- Write scientific and technical reports on project results.
- Contribute to defining the objectives and priorities of the BVSec project.

Compétences

- Knowledge of deep learning techniques and binary data processing.
- Proficiency in Python and C/C++ for developing AI systems.
- Experience in software security, vulnerability detection, and malware identification.
- Ability to prepare and analyze datasets for model evaluation.
- Familiarity with version control and collaborative project tools like Git and GitLab.
- Ability to work effectively within a multidisciplinary team.
- Independence and scientific rigor.
- Curiosity and a quick learner.
- Initiative and creativity in solving problems.
- Ability to communicate clearly and write technical or scientific reports.
- Leadership roles in research projects or team coordination.
- Ability to turn research findings into practical and applicable solutions

Contexte de travail

The VERIMAG laboratory is a joint research unit of the CNRS (UMR 5104) located on the Grenoble university campus, easily accessible from the city center by bike or public transportation. The laboratory has 70 staff members, including 36 permanent employees, divided into six research areas specializing in embedded systems and formal methods. The successful candidate will join the FETLAS team and report directly to Mr. Saddek BENSALEM.

The BVSec project creates artificial intelligence systems to automatically identify vulnerabilities and malware in software code. It merges advanced cybersecurity research with practical use, utilizing innovative binary file analysis and deep learning to improve the security of computer systems.

Contraintes et risques

Nothing

Informations complémentaires

More informations :
• An engaging work environment in contact with research staff
• On-site inter-company cafeteria with subsidized rates
• 44 days of vacation/RTT annually
• Remote work available (up to 2 days per week)
• Commitment to sustainable development
• A location accessible by public transport (tram B, regional and bus stops) plus private parking and bike storage
• Access to the CNRS Economic and Social Action Committee (social benefits, cultural and sporting activities: for more information, visit https://www.caes.cnrs.fr/ and https://caesug.grenoble.cnrs.fr/index.php)