13 offres internship trouvées dans Palaiseau, île-de-france

Although formal verification is essential for ensuring the safety and security of software, it remains difficult to deploy and use effectively by non-experts due to its steep learning curve. Recent advances in large language models (LLMs) have demonstrated remarkable abilities in code understanding, synthesis, and reasoning. These advances open promising research directions for assisting developers and verification engineers in formal specification and verification tasks. The goal of this internship is to explore and evaluate the integration of LLMs assistance into the Frama-C environment to support and automate parts of the specification and verification workflow. The work will focus on identifying the extent to which LLMs can provide meaningful support without compromising the rigor and reliability of formal program analysis. The following topics represent potential research and technical directions of the internship. Depending on the interests of the intern, one or more of them...

Context: Modern distributed systems, such as blockchain consensus and secure multiparty computation protocols, relies on a fundamental primitive: byzantine reliable broadcast. It allows a set of processes to agree on a message broadcasted by a dedicated process, even when some of them are malicious (byzantine). It relies on quorum systems to prevent correct processes from delivering two different messages: each message needs to be certified by a majority (a quorum) of processes before delivery. Byzantine reliable broadcast needs to maintain low latency and small communication complexity, but its efficiency depends on the number of processes constituting a majority. It was proposed in [1] to validate each message by smaller quorums such that 2 quorums for 2 different messages will intersect only with high probability. This technique is not yet secure against fully rushing-adaptive adversaries: a malicious entity can learn the processes constituting a small quorum and corrupt them...

Annotationg in 3D such (detection, segmentation ), is required to train reliable perception models, which can be used in autonomous driving for example. However such a process requires multi-modal (camera, LIDAR, RADAR) visualization and annotation which is a complicated and costly process. This is why at CEA-List we aim to develop smart tools to facilitate this annotation process. The objective of the internship is to allow for multi-modal annotation and visualization of 3D scenes. For example, annotate a scene observed by an autonomous vehicle with both cameras and LIDAR sensors. Develop both backend and front end for our open source annotation tool PIXANO to allow for : Multimodal visualization (camera / LIDAR / bev / 3D) Visualization of Nerf and/or Gaussian splatting Annotation in 2.5D at bird's eye view

Transciphering is a cryptographic technique that re-encrypts data from one scheme to another without an intermediate decryption step. This process drastically reduces the overhead induced by the size of homomorphic ciphertexts during their transmission and storage. The core idea of transciphering is to convert data encrypted with a classical symmetric encryption scheme into a Homomorphic Encryption (HE) scheme. How Transciphering Works ? Consider a scenario with a plaintext message m, a symmetric scheme SYM with key k, and a Homomorphic Encryption scheme HE with a public key pk. The message is initially encrypted as SYM.Enc_k(m). Transciphering uses the evaluation capability of the HE scheme to perform the symmetric decryption process entirely within the homomorphic domain. The process requires the symmetric key k to be encrypted under HE public key, HE.Enc_pk(k). By using the homomorphic evaluation key evk, the result is a message encrypted directly under HE public key pk:...

The detection of out-of-distribution (OoD) samples is crucial for deploying deep learning (DL) models in real-world scenarios. OoD samples pose a challenge to DL models as they are not represented in the training data and can naturally arrive during deployment (i.e., a distribution shift), increasing the risk of obtaining wrong predictions. Consequently, OoD samples detection is crucial in safety-critical tasks, such as healthcare or automated vehicles, where trustworthy models are required. The existing literature for the OoD detection problem focuses on the development of confidence scores where a threshold is applied to build a binary classifier to tell if a sample is in-distribution (InD) or OoD. In particular, the confidence score threshold is typically set using the values that correspond to InD samples, such that 95% of the confidence score values from InD samples fall above the selected thresholds, i.e., 95% True Positive Rate. However, setting a fixed threshold can lead...

Objective: The goal of this internship is to design and evaluate a new intelligent scheduling strategy using reinforcement learning (RL). The idea is to enable the system to learn how to make smarter scheduling decisions over time, optimizing container placement and sizing, dynamic resource allocation, response time and energy consumption and even inter-container dependencies such as shared data or communication patterns. Your missions: During this internship, you will: Explore and understand the orchestration framework developed within the team. Conduct a state-of-the-art study on RL-based scheduling in cloud and distributed environments. Design, implement, and train a new RL-based scheduler. Develop a feature extraction module to characterize container behavior and guide the RL agent's decisions. Evaluate your approach through experiments and benchmark comparisons

Root-Cause Analysis (RCA) is a systematic process for identifying the fundamental cause of a problem or failure, rather than merely addressing its symptoms. It aims to understand why something went wrong in order to take appropriate actions and prevent recurrence. RCA is essential for robots that operate outside strictly controlled environments, where they are inevitably confronted with unexpected situations and failures. Symptoms can range widely, including erratic movements, sudden halts, or suboptimal task outcomes. RCA distinguishes these symptoms from the actual causes, which may include hardware or software bugs, inaccurate behavior specifications, or environmental factors. By pinpointing the root cause, robots can select appropriate goals for repair or system adjustments. This informed decision-making enhances resilience and ensures long-term safe autonomy for robots. Causal inference is a branch of AI research that focuses on understanding and modeling cause-and-effect...

Your missions within this internship are to: - Study state-of-the-art methods of Open Set Object Detection (OSOD) as well as Visual Language Models (VLM) in the context of Open World containing both known and unknown objects; - Design an object detector aware of the existence of the unknown, and able to describe the unknown by comparing it to or distinguishing it from what is known via certain characteristics that can be described textually; - Evaluate the proposed method on recent OSOD benchmarks and compare to the state of the art; - Challenge these methods by applying them to new contexts (e.g. aerial images, medical imaging); - If relevant, submit your contributions to an international conference or workshop for publication. Join CEA List and LVA as an intern to: - Work in one of the most innovative research organizations in the world, addressing societal challenges to build the world of tomorrow - Discover a rich ecosystem: privileged connections between the industrial and...

Please view a better description on: https://kalisteo.cea.fr/index.php/offres-demploi/ CEA recently invented a new 3D hyperspectral imager for moving objects. This imager requires specific reconstruction code to operate. We have the theoretical formulation of the solution but still lack a proper implementation and testing. During this internship, you will develop code to exploit this imager for 3D reconstruction and spectral information extraction. You may have to propose improvements to the optical system. Reviewing existing literature on the subject Implementing the base 3D+spectral reconstruction code Producing scientific content (publications, patents, databases, etc.)

See illustrations on: https://kalisteo.cea.fr/index.php/offres-demploi/ During this internship, you will investigate state of the art techniques for detection of objects in images when the CAD model of the object is available (CAD-conditioned detection). Indeed, most state of the art approaches start with an object-agnostic segmentation (using Meta's SAM for instance) followed by template matching. But this does not work in industrial contexts, where objects are not cars, dogs or other everyday classes easily distinguishable from the background. The internship will notably include: Reviewing existing literature on the subject Downloading and testing the best approaches identified Implementing existing approaches lacking proper open-source implementation Creating, implementing and benchmarking new approaches

Version française Pour répondre aux exigences de l'Industrie 4.0 (robotique, automatisation), l'intégration du standard Time-Sensitive Networking (TSN) aux réseaux 5G est devenue une priorité. Le système 5G doit garantir un service déterministe en agissant comme un pont TSN. Ce stage se concentre sur le développement d'un composant logiciel clé : la TSN Application Function (TSN-AF), chargée de traduire les exigences de communication industrielle en politiques de Qualité de Service (QoS) pour le coeur de réseau 5G (5GC). Les travaux seront réalisés sur la plateforme open-source de référence OpenAirInterface (OAI). L'objectif principal est de concevoir, développer et valider la fonction TSN-AF et de l'intégrer au coeur de réseau 5G OAI. Les tâches clés sont : - Analyse de Spécifications : étude approfondie des standards 3GPP concernant l'intégration 5G-TSN pour maîtriser les mécanismes de QoS, les modèles d'architecture et les interfaces requises.. - Conception de l'Architecture :...

Histoire d’Or est le leader sur le marché de la bijouterie horlogerie en France . Notre succès réside dans la promesse de rendre le bijou accessible au plus grand nombre. Avec plus de 350 magasins en France, Histoire d’Or connait un fort développement et recrute. Nous recherchons des stagiaires pour accompagner nos clients à travers leurs plus belles Histoires. Ce sera pour vous l'occasion de faire vos premiers pas dans l'univers raffiné de la bijouterie / Horlogerie. Vous effectuez une formation dans le secteur du commerce (BTS, Bac pro, CAP) et recherchez un stage en vente Vous êtes curieux et souhaitez découvrir un métier riche et passionnant Vous êtes intéressé par la relation client et aimez rendre service Vous avez le goût du challenge et appréciez le travail en équipe L’univers de la bijouterie horlogerie vous intéresse Vous l’aurez compris, chez Histoire d’Or, c’est votre personnalité qui est déterminante. Vous nous apportez votre dynamisme, votre sourire et...

Stage Ingenieur(e)Structuration de processus et d'outils pour le pilotage de l'exploitation F/H Maintenance Rejoignez la grande aventure du métro automatique en Île-de-France avec Keolis ! Dès fin 2024, Keolis Compagnie du Métro du Grand Paris (KCMGP-L18) se mobilise pour préparer la mise en service de la ligne 18 du Grand Paris Express prévue fin 2026. Rejoindre l’équipe KCMGP, c’est contribuer au projet unique de lancement de deux nouvelles lignes de métro automatique en Île-de-France. Responsable de l’accueil et de l’assistance des voyageurs en gare, vous interviendrez sur les équipements du métro pour délivrer un service clients et une performance opérationnelle aux meilleurs standards internationaux . Grace à vos actions, vous serez les bâtisseurs de la mise en place d’une culture d’excellence pour proposer aux voyageurs franciliens une nouvelle expérience : celle du métro du 21ème siècle, plus efficace, plus connecté et plus humain Missions Votre mission...