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phD
3D ultrasound imaging using orthogonal row and column addressing of the matrix array for ultrasonic NDT
This thesis is part of the activities of the Digital Instrumentation Department (DIN) in Non-Destructive Testing (NDT), and aims to design a new, fast and advanced 3D ultrasound imaging method using matrix arrays. The aim will be to produce three-dimensional ultrasound images of the internal volume of a structure that may contain defects (e.g. cracks),...
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phD
High-throughput experimentation applied to battery materials
High throughput screening, which has been used for many years in the pharmaceutical field, is emerging as an effective method for accelerating materials discovery and as a new tool for elucidating composition-structure-functional property relationships. It is based on the rapid combinatorial synthesis of a large number of samples of different compositions, combined with rapid and...
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phD
In-Sensor Computing for MEMS Sensors: Toward an Electromechanical Neural Network
The rise of machine learning models for processing sensor data has led to the development of Edge-AI, which aims to perform these data processing tasks locally, directly at the sensor level. This approach reduces the amount of data transmitted and eases the load on centralized computing centers, providing a solution to decrease the overall energy...
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phD
Topologic optimization of µLEDapos;s optical performance
The performance of micro-LEDs (µLEDs) is crucial for micro-displays, a field of expertise at the LITE laboratory within CEA-LETI. However, simulating these components is complex and computationally expensive due to the incoherent nature of light sources and the involved geometries. This limits the ability to effectively explore multi-parameter design spaces. This thesis proposes to develop...
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phD
Development of multiplexed photon sources for quantum technologies
Quantum information technologies offers several promises in domains such as computation or secured communications. There is a wide variety of technologies available, including photonic qubits. The latter are robust against decoherence and are particularly interesting for quantum communications applications, even at room temperature. They also offers an alternative to other qubits technologies for quantum computing....
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phD
Learning world models for advanced autonomous agent
World models are internal representations of the external environment that an agent can use to interact with the real world. They are essential for understanding the physics that govern real-world dynamics, making predictions, and planning long-horizon actions. World models can be used to simulate real-world interactions and enhance the interpretability and explainability of an agentapos;s...
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phD
Adaptive and explainable Video Anomaly Detection
Video Anomaly Detection (VAD) aims to automatically identify unusual events in video that deviate from normal patterns. Existing methods often rely on One-Class or Weakly Supervised learning: the former uses only normal data for training, while the latter leverages video-level labels. Recent advances in Vision-Language Models (VLMs) and Large Language Models (LLMs) have improved both...
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phD
Grounding and reasoning over space and time in Vision-Language Models (VLM)
Recent Vision-Language Models (VLMs) like BLIP, LLaVA, and Qwen-VL have achieved impressive results in multimodal tasks but still face limitations in true spatial and temporal reasoning. Many current benchmarks conflate visual reasoning with general knowledge and involve shallow reasoning tasks. Furthermore, these models often struggle with understanding complex spatial relations and dynamic scenes due to...
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phD
Physics informed deep learning for non-destructive testing
This PhD project lies within the field of Non-Destructive Testing (NDT), which encompasses a range of techniques used to detect defects in structures (cables, materials, components) without causing any damage. Diagnostics rely on physical measurements (e.g., reflectometry, ultrasound), whose interpretation requires solving inverse problems, which are often ill-posed. Classical approaches based on iterative algorithms are...
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phD
AI Enhanced MBSE framework for joint safety and security analysis of critical systems
Critical systems must simultaneously meet the requirements of both Safety (preventing unintentional failures that could lead to damage) and Security (protecting against malicious attacks). Traditionally, these two areas are treated separately, whereas they are interdependent: An attack (Security) can trigger a failure (Safety), and a functional flaw can be exploited as an attack vector. MBSE...
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phD
Robust and Secure Federated Learning
Federated Learning (FL) allows multiple clients to collaboratively train a global model without sharing their raw data. While this decentralized setup is appealing for privacy-sensitive domains like healthcare and finance, it is not inherently secure: model updates can leak private information, and malicious clients can corrupt training. To tackle these challenges, two main strategies are...
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phD
Towards Reliable and Autonomous Workflow Coordination in Agentic AI-Based Systems
The rise of Large Language Models (LLMs) and agentic AI systems is transforming how complex workflows are designed and managed. Unlike traditional centralized orchestration, modern workflows must support distributed, autonomous agents operating across cloud, edge, and on-premise environments. These agents collaborate with humans and other systems, adapt to evolving goals, and cross organizational and trust...
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phD
GenPhi : 3D Generative AI conditioned by geometry, structure and physics
The aim of this thesis is to design new 3D model generators based on Generative Artificial Intelligence (GenAI), capable of producing faithful, coherent and physically viable shapes. While 3D generation has become essential in many fields, current automatic generation approaches suffer from limitations in terms of respecting geometric, structural and physical constraints. The goal is...
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phD
Long-term and non-invasive plant monitoring using MIR spectroscopy
The LCO (french acronym for Optical Sensors Laboratory) develops innovative Silicium integrated photonic components (optical sources, waveguides, photodetectors, etc), sensors, and eventually systems. From upstream technological research to industrial transfers, those sensors apply in various fields such as environment, health, and security. One of the laboratory research topic is mid-infrared spectroscopy of dense samples, using...
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phD
Bridging the embedding gap between expressive specification and efficient verification of machine learning
Formal verification of neural networks is facing a double-faceted issue. The expressiveness of specifications (as in: compact and close to human understanding) is apparently clashing with their efficient translation to state-of-the-art prover, who only support a fragment of arithmetic without quantifiers. This thesis will investigate quot;globalquot; properties. Such class of properties describe generic behaviours of...