About this role
At STMicroelectronics, we believe in the power of technology to drive innovation and make a positive impact on people, businesses, and society. As a global semiconductor company, our advanced technologies and chips form the hidden foundation of the world we live in today.
When you join ST, you will be part of a global business with more than 115 nationalities, present in 40 countries, and comprising over 50,000 diverse and dedicated creators and makers of technology around the world.
Developing technologies takes more than talent: it takes amazing people who understand collaboration and respect. People with passion and the desire to disrupt the status quo, drive innovation, and unlock their own potential.
Embark on a journey with us, where you can innovate for a future that we want to make smarter and greener, in a responsible and sustainable way. Our technology starts with you.
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English Description
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CONTEXT
Artificial intelligence and deep neural networks are increasingly deployed in embedded systems, enabling critical applications such as face recognition, anomaly detection, and autonomous driving. However, over time, the information and features initially learned by these devices may become less relevant, and the models may require retraining, adaptation, or personalization. This is particularly true in industrial settings, where sensors collect data that may drift over time, for example due to changes in temperature, environmental conditions, or operating parameters.
For this reason, an increasing number of research efforts aim to update the parameters of a neural network throughout its lifetime. In such cases, learning is performed directly on the device, using new input data that are processed locally. Nonetheless, the growing adoption of these approaches has also led to the emergence of new attack paradigms, such as poisoning attacks.
YOUR ROLE
Within an R&D team specialized in embedded systems security, during this 3 to 6-month internship, you will focus on developing countermeasures and solutions to defend against attacks that may be crafted during the training phase of AI-based systems. You will first conduct a state-of-the-art review of existing approaches, then identify and develop promising methods, and finally evaluate their robustness and efficiency on microcontroller-based platforms.
This work will combine machine learning, security, and embedded experimentation, with the potential to lead to a PhD thesis proposal.
YOUR SKILLS & EXPERIENCES
- Master degree (1st or 2nd year) in computer science or a related field.
- Knowledge in deep learning.
- Skills in Python and embedded systems.
- Proactivity, autonomy, and teamwork.
- Written and spoken English.
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French Description
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CONTEXTE
L’intelligence artificielle et les réseaux de neurones profonds sont de plus en plus déployés dans les systèmes embarqués, instanciés dans des applications critiques telles que la reconnaissance faciale, la détection d’anomalies et la conduite autonome. Cependant, au fil du temps, les informations et les caractéristiques initialement apprises par ces dispositifs peuvent devenir moins pertinentes, et les modèles peuvent nécessiter un ré-entraînement, une adaptation ou une personnalisation. Cela est particulièrement vrai dans les environnements industriels, où les capteurs collectent des données susceptibles de dériver au cours du temps, par exemple en raison de variations de température, de conditions environnementales ou de paramètres de fonctionnement.
C’est pourquoi un nombre croissant de travaux de recherche visent à mettre à jour les paramètres du réseau de neurones tout au long de son cycle de vie. Dans ce cas, l’apprentissage est réalisé directement sur le dispositif, à partir de nouvelles données d’entrée traitées localement. Néanmoins, l’adoption croissante de ces approches a également conduit à l’émergence de nouveaux paradigmes d’attaque, tels que les attaques par empoisonnement.
VOTRE ROLE
Au sein d’une équipe R&D spécialisée dans la sécurité des systèmes embarqués, ce stage de 3 à 6 mois vous permettra d’étudier des contre-mesures et des solutions visant à se défendre contre des attaques pouvant être élaborées durant la phase d’entraînement des systèmes basés sur l’IA.
Vous réaliserez dans un premier temps l’étude de l’état-de-l’art des approches existantes, puis identifierez et développerez des méthodes prometteuses, avant d’évaluer leur robustesse et leur efficacité sur des plateformes microcontrôleurs.
Ce travail combinera Machine Learning , sécurité et expérimentation embarquée, avec des perspectives possibles pour une proposition de thèse de doctorat.
VOTRE PROFIL
- Première ou dernière année de master ou équivalent en computer science ou similaire.
- Connaissances en apprentissage profond.
- Compétences en programmation Python et en systèmes embarqués.
- Proactivité, autonomie et travail d’équipe.
- Anglais écrit et parlé.
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ST is proud to be one of the 17 companies certified as a 2025 Global Top Employer and the first and only semiconductor company to achieve this distinction. ST was recognized in this ranking thanks to its continuous improvement approach and stands out particularly in the areas of ethics & integrity, purpose & values, organization & change, business strategy, and performance.
At ST, we endeavor to foster a diverse and inclusive workplace, and we do not tolerate discrimination. We aim to recruit and retain a diverse workforce that reflects the societies around us. We strive for equity in career development, career opportunities, and equal remuneration. We encourage candidates who may not meet every single requirement to apply, as we appreciate diverse perspectives and provide opportunities for growth and learning. Diversity, equity, and inclusion (DEI) is woven into our company culture.
To discover more, visit st.com/careers.