4 offres drt france trouvées dans Palaiseau, île-de-france

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...

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

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

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...