Simplified Model for Rotary Tube Calcination
Since the vitrification lines at La Hague began operation in 1989, ORANO (formerly AREVA) has faced difficulties in controlling the calciner. Actions taken to significantly reduce these problems have considerably eased them, but without completely eliminating them. Most of the recommended actions are based on expert opinions, which themselves are based on inactive test results that don't cover all situations encountered by ORANO. To definitively resolve these control difficulties, it was decided to launch a more theoretical modeling study, while simultaneously investigating new calciner control instrumentation.
Microfluidics for biomimetic detection of airbone pathogens
Air represents a complex contamination pathway that is difficult to control and through which numerous biological, biochemical, or chemical agents can affect populations and healthcare workers. Standard detection approaches, whether qPCR, antigen tests, or ELISA tests, rely on reagents specific to known and targeted agents. These approaches are therefore unsuitable for detecting an unknown pathogen that could result in a new pandemic. To face such unknown agents, new biosensors will be needed to distinguish between pathogenic and non-pathogenic agents. Also, these sensors will have to be miniature for deployment.
With a new microfluidic system the present project aims to explore original approaches for conducting such detection without preconceived notions. Based on the laboratory's experience and developments, the PhD will include :
- developing new materials and designs to optimize and to enable multiple bioaerosol sampling;
- developing a biomimetic biochip and optimize molecular interactions using microflows controlled at the micro/milliscale.
You will design a microfluidic card integrating new detection strategies and study them experimentally using prototypes already developed in the laboratory.
Optimization of transports in the Gas Diffusion Layers of Proton Exchange Membrane Fuel Cells: Artificial Intelligent as a support to define optimal porous structures and usage
The design and manufacturing of innovative materials with required properties is a key objective for developing advanced technologies in the field of energy, such as Hydrogen and Alcaline fuel cells and Electrolysers. These improvements will contribute to propose even more attractive low-carbon electrical energy systems, with reduced pollution and green-house effects.
This thesis focuses on the Gas Diffusion Layer (GDL) which plays a crucial role on the performance and durability of Proton Exchange Membrane Fuel Cell (PEMFC).
Your main aim will be to set-up a numerical approach so as to propose improved porous structures to optimize the different transports inside a GDL, for given targets and constraints. To do so, you will make the bridge between advanced modeling of (electrical, heat, liquid, gas) transports in 3D porous media and artificial intelligence. You will then analyze the influence of the operating conditions on such optimal structures and propose design recommendations.
This work will be conducted in close relationship between world-renowned scientific actors : the fuel cells and the modelling teams of CEA/LITEN (Grenoble), the specialists of transports in porous media at CNRS/IMFT (Toulouse), and the specialists of GDL, modeling and AI at FZJ (Juelich, https://www.fz-juelich.de/en).
Scientific publications are expected and patents could also be proposed.
Machine Learning-Accelerated Electron Density Calculations
Density Functional Theory (DFT) in the Kohn-Sham formalism is one of the most widespread methods for simulating microscopic properties in solid-state physics and chemistry. Its main advantage lies in its ability to strike a favorable balance between accuracy and computational cost. The continuous evolution of increasingly efficient numerical techniques has constantly broadened the scope of its applicability.
Among these techniques that can be associated with DFT, machine learning is being used more and more. Today, a very common application consists in producing potentials capable of predicting interactions between atoms using supervised learning models, relying on properties computed by DFT.
The objective of the project proposed as part of this thesis is to use machine learning techniques at a deeper level, notably to predict the electronic density in crystals or molecules. Compared to predicting properties such as forces between atoms, calculating the electronic density presents certain challenges: the electronic density is high-dimensional since it must be calculated throughout all space; its characteristics vary strongly from one material to another (metals, insulators, charge transfer, etc.). Ultimately, this can represent a significant computational cost. There are several options to reduce the dimensionality of the electronic density, such as computing projections or using localization functions.
The final goal of this project is to be able to predict, with the highest possible accuracy, the electronic density, in order to use it as a prediction or as a starting point for calculations of electron-specific properties (magnetism, band structure, for example).
In a first stage, the candidate will be able to implement methods recently proposed in the literature; in a second part of the thesis, it will then be necessary to propose new ideas. Finally, the implemented method will be used to accelerate the prediction of properties of large systems involving charge transfers, such as defect migration in crystals.
Automatic modelling language variations for socially responsive chatbots
Conversational agents are increasingly present in our daily lives thanks to advances in natural language processing and artificial intelligence and are attracting growing interest. However, their ability to understand human communication in all its complexity remains a major challenge. This PhD project aims to model linguistic variation to develop agents capable of socially adaptive interactions, taking into account the socio-demographic profile and emotional state of their interlocutors. It also focuses on evaluating linguistic cues at different levels, leveraging both spoken and written language varieties, and assessing the generalization capacity of models trained on multilingual and multi-situational data, with the goal of improving interaction modeling with conversational agents.
Compositional Generalization of Visual Language Models
The advent of the foundation models led to increase the state-of-the art performance on a large number of tasks in several fields of AI, in particular computer vision and natural language processing. However, despite the huge amount of data used to train them, these models are still limited in their ability to generalize, in particular for a use case of interest that is in a specific domain, not well represented on the Web. A way to formalize this issue is compositional generalization, i.e. generalising to a new, unseen concept from concepts learned during training. This "generalization" is the ability to learn disentangle concepts and to be able to recombine
them into unseen composition when the model is in production. The proposed thesis will address this issue, aiming at proposing visual representations that enable generic visual language models to generalize compositionally within specific domains. It will investigate strategies to reduce shortcut learning, promoting deeper understanding of compositional structures in multimodal data. It will also address the problem of compositional generalization beyond simple attribute–object pairs, capturing more subtle and complex semantics. The proposed thesis aims at proposing preogress at a quite theoretical level but has many potential practical interest, in the fields of health, administration and services sectors, security and defense, manufacturing and agriculture.
Artificial Intelligence for Integrated Electronics Design
As the technology fabrication processes improve towards nanometer-scale nodes, it is more and more complex to maintain the performance increase foreseen by Moore Law. To cope with this issue, technology processes provide various enhancing featuresi. More over, elementary components such as logic gates become legion. Providing a relevant design framework thus becomes a huge manual development task. As AI grows, it shows its skill to help decision making and hence components design, shaping a promising candidate to automate design flow. In this PhD subject, you will work on an AI model (LLM) capable of understanding electronic components. The works ultimately aim at developing a generation engine for electronic components.
Throughout this PhD, interdisciplinary research works will encompass a broad spectrum of knowledge around integrated electronics design, spanning microelectronics processes, electronic functions and logic gates implementation, neural networks architectures, large language models and generative AI.
Towards real-time simulation of thermal scenes in a tokamak to support plasma operations.
Monitoring the surface temperatures and heat fluxes of the walls in nuclear fusion devices is crucial for the operation of fusion machines. To ensure the reliability of these measurements, particularly through infrared imaging, CEA is developing a digital twin capable of modeling the entire infrared (IR) measurement chain, from the thermal source to the sensor.
The objective of this thesis is to create a thermal model that can predict heat fluxes and surface temperatures across the entire machine wall, with a goal of real-time computation. This approach is based on two key developments:
1)Development of a Monte Carlo statistical method: This method will solve the heat equation over large geometries in a complex environment, including a variety of heat sources and materials.
2)Acceleration of calculations on graphics processing units (GPU): Utilization of the Kokkos environment to optimize calculation performance while ensuring portability across all high-performance computing (HPC) platforms.
These developments will be validated and quantitatively evaluated on two experimental platforms: the laboratory test bench MAGRYT and the WEST tokamak, used as a demonstrator machine. The thesis will be conducted in a collaborative framework between CEA/DRF/IRFM and CEA/DES/ISAS. The developments will be integrated into the IR digital twin developed by CEA/IRFM for fusion machines and within a dedicated ray-tracing application for CEA/DES.
Towards a low-resistive base contact for the InP-HBT transistor
Join CEA LETI for an exciting technological journey! Immerse yourself in the world of III V
based transistors integrated on compatible CMOS circuits for 6 G future communications
This thesis offers the chance to work on a ambitious project, with potential to continue into
a thesis If you're curious, innovative, and eager for a challenge, this opportunity is perfect
for you!
As the consumption of digital content continues to grow, we can foresee that 6 G
communication systems will have to find more capacity to support the increase in traffic
New Sub THz frequencies based systems are a huge opportunity to increase data rate but
are very challenging to build and maturate the power amplifier required to transmit a
signal will have to offer sufficient power and energy efficiency which is not obtained with
actual silicon technology InP based HBTs (Heterojunction Bipolar Transistors) developed
on large Silicon substrates have the potential to meet the requirements and be integrated
as close as possible to the CMOS circuits to enable minimal system/interconnect losses
Sb based semiconductors for GaAsSb HBT are emerging as highly promising materials,
especially for its electrical properties to integrate the Base layer of the Transistor It is
therefore necessary to produce high performance electrical contacts on this type of
semiconductor while remaining compatible with the manufacturing processes of the Si Fab
technology platforms
Throughout
this thesis, you will gain a broad spectrum of knowledge, beneficiate from the
rich technical environment of the 300 200 mm clean room and the nano characterization
platform You will collaborate with multidisciplinary teams to develop a deep understanding
of the ohmic contacts and analyse existing measurements Several apsects of the metal
(Ni or Ti p GaAs 1 x Sb x contact will be investigated
•Identify wet and plasma solutions allowing the GaAsSb native oxide removing without
damaging the surface with XPS and AFM
•Characterize GaAs 1 x Sb x epitaxy doping level (Hall effect, SIMS, TEM)
•Understand the phase sequence during annealing between the semiconductor and the
metal with XRD and Tof SIMS Manage this intermetallic alloys formation to not
deteriorate the contact interface (TEM image associated)
•Evaluate electrical contact properties using TLM structures Measurement of the
specific contact resistivity, sheet resistance of the semiconductor ant transfer length
associated The student will be a motive force to perform electrical tests on an automatic prober
architecture for embedded system of Automated and Reliable Mapping of indoor installations
The research focuses on the 3D localization of data from measurements inside buildings, where satellite location systems, such as GPS, are not operational. Different solutions exist in the literature, they rely in particular on the use of SLAM (Simultaneous Localization And Mapping) algorithms, but the 3D reconstruction is generally carried out a posteriori. In order to be able to propose this type of approach for embedded systems, a first thesis was carried out and led to a choice of algorithms to embed and a draft of the electronic architecture. A first proof of concept was also realized. Continuing this work, the thesis will have to propose a method allowing the localization device to be easily embedded on a wide range of nuclear measuring equipment (diameter, contamination meter, portable spectrometry, etc.). The work is not limited to a simple integration phase; it requires an architectural exploration, which will be based on adequacy between algorithm and architecture. These approaches will make it possible to respect different criteria, such as weight and small size so as not to compromise ergonomics for the operators carrying out the maps and quality of the reconstruction to ensure the reliability of the input data for the Digital Twin models.