Characterization of motor recovery in stroke patients during a BCI-guided rehabilitation
Brain-computer interfaces (BCIs) make it possible to restore lost functions by allowing individuals to control external devices through the modulation of their brain activity. The CEA has developed a BCI technology based on the WIMAGINE implant, which records brain activity using electrocorticography (ECoG), along with algorithms for decoding motor intentions. This technology was initially tested for controlling robotic effectors such as exoskeletons and spinal cord stimulation devices to compensate for severe motor impairments. While this initial paradigm of substitution and compensation is promising, a different application potential is now emerging: functional recovery through BCI-guided rehabilitation. Current literature suggests that BCIs, when used intensively and in a targeted manner, can promote neural plasticity and, in turn, improve residual motor abilities. In particular, ECoG-based implanted BCIs could offer significant therapeutic outcomes. The objective of this thesis is therefore to assess the potential of CEA's BCI technology to enhance patients' residual motor functions through neural plasticity.
This work will be approached through a rigorous and multidisciplinary scientific methodology, including a comprehensive review of the scientific literature, the setup and execution of experimentations with patients, the algorithmic development of tools for monitoring and analyzing patient progress, and the publication of significant results in high-level scientific journals.
This PhD is intended for a student specializing in biomedical engineering, with expertise in signal processing and the analysis of complex physiological data, as well as experience in Python or Matlab. A strong interest in clinical experimentation and neuroscience will also be required. The student will work within a multidisciplinary team at CLINATEC, contributing to cutting-edge research in the field of BCIs.
Accelerating thermo-mechanical simulations using Neural Networks --- Applications to additive manufacturing and metal forming
In multiple industries, such as metal forming and additive manufacturing, the discrepancy between the desired shape and the shape really obtained is significant, which hinders the development of these manufacturing techniques. This is largely due to the complexity of the thermal and mechanical processes involved, resulting in a high computational simulation time.
The aim of this PhD is to significantly reduce this gap by accelerating thermo-mechanical finite element simulations, particularly through the design of a tailored neural network architecture, leveraging theoretical physical knowledge.
To achieve this, the thesis will benefit from a favorable ecosystem at both the LMS of École Polytechnique and CEA List: internally developed PlastiNN architecture (patent pending), existing mechanical databases, FactoryIA supercomputer, DGX systems, and 3D printing machines. The first step will be to extent the databases already generated from finite element simulations to the thermo-mechanical framework, then adapt the internally developed PlastiNN architecture to these simulations, and finally implement them.
The ultimate goal of the PhD is to demonstrate the acceleration of finite element simulations on real cases: firstly, through the implementation of feedback during metal printing via temperature field measurement to reduce the gap between the desired and manufactured geometry, and secondly, through the development of a forging control tool that achieves the desired geometry from an initial geometry. Both applications will rely on an optimization procedure made feasible by the acceleration of thermo-mechanical simulations.
Physics of perovskite materials for medical radiography: experimental study of photoconduction gain
X-rays is the most widely used medical imaging modality. It is used to establish diagnostics, monitor the evolution of pathologies, and guide surgical procedures.
The objective of this thesis is to study a perovskite type semiconductor material for its use as a direct X-ray sensor. Perovskite-based matrix imagers demonstrate improved spatial resolution and increased signal, and can thus help improve patient treatment. Prototype X-ray imagers manufactured at the CEA already provide radiographic images but their performances are limited by the instability of the sensor material.
You job will be to study the mechanisms responsible for the photoconduction gain and photocurrent drift of thick perovskite layers from both a theoretical and an experimental standpoint. To this end, you will adapt the electro-optical characterization benches of the laboratory, conduct experiments and analyze the data collected. You will also have the opportunity to perform advanced characterizations with specialized laboratories within the framework of national and international collaborations. The results of this thesis will provide a better understanding of the material properties and guide its ellaboration to produce high-performance X-ray imagers.
Thermomechanical study of heterostructures according to bonding conditions
For many industrial applications, the assembly of several structures is one of the key stages in the manufacturing process. However, these steps are generally difficult to carry out, as they lead to significant increases in warpage. Controlling stresses and strains generated by heterostructures is however imperative. We proposes to address this subject using both experimental exploration and simulation through thermomechanical studies in order to predict and anticipate problems due to high deformations.
Design and implementation of cryogenic electronics for signal acquisition at cryogenic temperatures
The aim of our proposed thesis is to demonstrate that it is possible to integrate at cryogenic temperatures the entire instrumentation chain for reading and controlling quantum components at cryogenic temperatures
qubits. In other words, we are seeking to place in-situ, in the cryostat and as close as possible to the quantum components
(qubits), all the systems that are currently located outside. In addition, to achieve a major breakthrough
we are aiming for a fully programmable microwave chain (> 2 GHz). This is the subject of an ongoing thesis
financed by the Agence Innovation Défense (AID) and the Commissariat à l'Énergie Atomique (CEA) and a RAPID-type project application.
RAPID type project.
As part of this thesis, we will start at a few hundred MHz. Several main problems
are identified and need to be solved, including
- design and integration of chiplets in System-in-Packages (SiPs) compatible with cryogenic temperatures ;
- interfacing and integrating the Analog to Digital Converter (ADC), Digital to Analog
Converter (DAC) and processing components;
- manage high data rates (several tens of Gbit/s per qubit);
- maximum roundtrip latency of 200 ns;
- energy management (a few tens of mW budget per qubit);
- choice of cryogenic stages adapted to the different processing stages;
- choice of independent technologies