Jobs
All our offers
-
phD
Artful guidance of test generation tools
Fuzzing is an automatic test generation technique. It consists in repeatedly executing a program with automatically generated inputs, in order to trigger crashes, symptoms of underlying bugs in the code, which can then be fixed. A major challenge in this area is moving from indiscriminate exploration of how programs work to artful guidance towards the...
-
phD
AI for SEM metrology: image generation and 3D reconstruction applied to microelectronic devices
Scanning Electron Microscopy (SEM) imaging is the current reference method for quality control in the microelectronic industry, due to the size of the objects involved and to the yield expected when these tools are used in production lines. In order to improve our knowledge on physics in play during imaging and to develop more performant...
-
phD
Security-by-design for embedded deep neural network models on RISC-V
With a strong context of regulation of Artificial Intelligence (AI) at the European scale, several requirements have been proposed for the quot;cybersecurity of AIquot;. Among the most important concepts related to the security of the machine learning models and the AI-based systems, quot;security-by-designquot; is mostly linked to model hardening approaches (e.g., adversarial training against evasion...
-
phD
Deep Learning Models for Decoding of LDPC Codes
Error correction coding (ECC) has an essential role in ensuring integrity of data in numerous applications, including data storage, transmission, and networking. Over the last few years, new interactions emerged between coding theory and machine learning, seen as a promising way to overcome the limitations of existing ECC solutions at short to medium code-lengths. For...
-
phD
Learning Fine-Grained Dexterous Manipulation through Vision and Kinesthetic Observations
Fine-grained dexterous manipulation presents significant challenges for robots due to the need for precise object handling, coordination of contact forces, and utilization of visual observations. This research aims to address these challenges by investigating the integration of vision and kinesthetic sensors, sim2real techniques, and generalization through embodiment. The objective is to develop end-to-end algorithms and...
-
phD
Deep Neural Network Uncertainty Estimation on Embedded Targets
Over the last decade, Deep NeuralNetworks (DNNs) have become a popular choice to implement Learning-Enabled Components LECs in automated systems thanks to their effectiveness in processing complex sensory inputs, and their powerful representation learning that surpasses the performance of traditional methods. Despite the remarkable progress in representation learning, DNNs should also represent the confidence in...
-
phD
Electronic structure calculation with deep learning models
Ab initio simulations with Density Functional Theory (DFT) are now routinely employed across scientific disciplines to unravel the intricate electronic characteristics and properties of materials at the atomic level. Over the past decade, deep learning has revolutionized multiple areas such as computer vision, natural language processing, healthcare diagnostics, and autonomous systems. The combination of these...
-
phD
Generative artificial intelligence algorithms for understanding and countering online polarization
Digital platforms enable the widespread dissemination of information, but their engagement-centric business models often promote the spread of ideologically homogeneous or controversial political content. These models can lead to the polarization of political opinions and impede the healthy functioning of democratic systems. The PhD will investigate innovative generative AI models devised for a deep understanding...
-
phD
Combination study of high throughput screening techniques and artificial intelligence (AI) to identify innovative materials for next generation of battery
In recent years, the CEA has set up an experimental high-throughput screening (HTS) activity for lithium battery materials, based on combinatorial synthesis by sputtering and various high-throughput characterisation techniques on large substrates (typically 4 inches). Optimisation of material compositions is traditionally carried out by analysing experimental designs. In the framework of this thesis, we propose...
-
phD
Software and hardware acceleration of Neural Fields in autonomous robotics
Since 2020, Neural Radiance Fields, or NeRFs, have been the focus of intense interest in the scientific community for their ability to implicitly reconstruct 3D and synthesize new points of view of a scene from a limited set of images. Recent scientific advances have drastically improved initial performance (reduction in data requirements, memory needs and...
-
phD
AI-assisted generation of Instruction Set Simulators
The simulation tools for digital architectures rely on various types of models with different levels of abstraction to meet the requirements of hardware/software co-design and co-validation. Among these models, higher-level ones enable rapid functional validation of software on target architectures. Developing these functional models often involves a manual process, which is both tedious and error-prone....
-
phD
Tool supported model completion with support for design pattern application
Generative AI and large language models (LLMs), such Copilot and ChatGPT can complete code based on initial fragments written by a developer. They are integrated in software development environments such as VS code. Many papers analyse the advantages and limitations of these approaches for code generation, see for instance Besides some deficiencies, the produced code...
-
phD
The technology choice in the eco-design of AI architectures
Electronic systems have a significant environmental impact in terms of resource consumption, greenhouse gas emissions and electronic waste, all of which are experiencing a massive upward trend. A large part of the impact is due to production, and more particularly the manufacturing of integrated circuits, which is becoming more and more complex, energy-intensive and resource-intensive...
-
phD
Natural language interactions for anomaly detection in mono and multi-variate time series using fondation models and retrieval augmented generation
Anomaly detection in mono and multi-variate time series highly depends on the context of the task. State-of-the-art approaches rely usually on two main approaches: first extensive data acquisition is sought to train artificial intelligence models such as auto-encoders, able to learn useful latent reprensations able to isolate abnormality from expected system behaviors; a second approach...
-
phD
Low precision quantization of attention based neural network for embedded devices
Deploying artificial intelligence (AI) represents a major challenge. Over the last years, AI has developed using increasingly large neural networks and massive data processing. Today, the challenge is to adapt these methods to run on small embedded components and as close as possible to industrial solutions. The research question adressed here is how to make...
-
phD
Differentiable surrogate for simulation-based inference
Many models of complex phenomena (physics, molecular dynamics, etc.) have no global analytical expressions but admit implementations in silico in form of forward simulators. In turn, forward simulations are used to solve inverse problems: given observations of the phenomena find its initial conditions viewed as input parameters of the simulator. In statistical terms solving such...
-
phD
Clip approach for improving energy efficiency of hardware embedding combinations
In a global context of task automation, artificial neural networks are currently used in many domains requiring the processing of data from sensors: vision, sound, vibration. Depending on different constraints, the information processing can be done on the Cloud (SIRI, AWS, TPU) or in an embedded way (NVidiaapos;s Jetson platform, Movidius, CEA-LISTapos;s PNeuro/DNeuro). In this...
-
phD
Multimodal continual learning under constraints
Standard deep learning methods are designed to use static data. This induces a significant practical limitation when they are deployed in dynamic environments and are confronted with unknown data. Continuous learning provides a solution to this problem, especially with the use of large, pre-trained models. However, deploying such models in stand-alone mode is currently impossible...
-
phD
In-physics artificial intelligence using emerging nanodevices
Recent breakthroughs in models of AI are correlated with the energy burden required to define and run these models. GPUs are the goto hardware for these implementations, since they can perform configurable, highly parallelised and matrix multiplications using digital circuits. To go beyond the energy limits of GPUs however, it may be required to abandon...
-
phD
Quantum Machine Learning in the era of NISQ: can QML provide an advantage for the learning part of Neural Networks?
Quantum computing is believed to offer a future advantage in a variety of algorithms, including those challenging for traditional computers (e.g., Prime Factorization). However, in an era where Noisy Quantum Computers (QCs) are the norm, practical applications of QC would be centered around optimization approaches and energy efficiency rather than purely algorithmic performance. In this...
-
phD
Graph Neural Network-based power prediction of digital architectures
Performing power analysis is a major step during digital architecture development. This power analysis is needed as soon as the RTL (Register Transfer Level) coding starts, when the most rewarding changes can be made. As designs get larger, power analysis relies on longer simulation traces and becomes almost impossible, as the process generates huge simulation...
-
phD
In-memory analog computing for AI attention mechanisms
The aim of this thesis is to explore the execution of attention mechanisms for Artificial Intelligence directly within a cutting-edge Non-Volatile Memory (NVM) technology. Attention mechanisms represent a breakthrough in Artificial Intelligence (AI) algorithms and represent the performance booster behind “Transformers” neural networks. Initially designed for natural language processing, such as ChatGPT, these mechanisms are...
-
phD
Development and characterization of embedded memories based on ferroelectric transistors for neuromorphic applications
As part of CEA-LETI's Devices for Memory and Computation Laboratory (LDMC), you will be working on the development and optimization of FeFET transistors with amorphous oxide semiconductor channels for neuromorphic applications and near-memory computing. The main challenge when co-integrating semiconductor and ferroelectric oxides is to perfectly assess and control a proper amount of oxygen vacancies,...
EN 
FR 