MAP-RECOVER: Artificial intelligence-based prediction of post-stroke motor recovery using multimodal neuroimaging and brain-computer interfaces
MAP-RECOVER: Artificial intelligence-based prediction of post-stroke motor recovery using multimodal neuroimaging and brain-computer interfaces
Stroke is a leading cause of long-term motor disability, and many patients show limited benefit from conventional rehabilitation. This project aims to develop artificial intelligence methods to predict, early after stroke, which patients are most likely to benefit from brain-computer interface (BCI)-assisted rehabilitation.
The PhD candidate will use multimodal neuroimaging data, including ultra-high-field 7T fMRI, MEG, and ECoG recordings, to characterize brain lesions, motor networks, and the neural mechanisms underlying motor intention. Machine learning and deep learning approaches (e.g., convolutional neural networks, transformers, and multimodal fusion) will be developed to integrate these data, predict motor recovery, and identify biomarkers of rehabilitation potential. The models will be validated using clinical datasets from ongoing BCI rehabilitation studies.
Expected outcomes include predictive tools for personalized rehabilitation, improved patient selection for BCI therapies, and a better understanding of post-stroke neuroplasticity.
Candidate profile: Master's degree in Computational or Cognitive Neuroscience, Biomedical Engineering, Artificial Intelligence, or a related field. Strong programming skills in Python and experience with machine learning are required; knowledge of neuroimaging analysis tools is an asset.
The PhD will be carried out at Clinatec (CEA Grenoble, France) in collaboration with the Laboratory of Psychology and NeuroCognition (LPNC, CNRS, Université Grenoble Alpes).Link to post:https://www.linkedin.com/feed/update/urn:li:ugcPost:7480197083472338945
Analysis and quantification by XPS of active dopants in heavily doped epitaxial layers
RP-CVD epitaxy plays an important role in microelectronics because it allows the growth of heavily doped layers while maintaining the crystalline orientation of the starting substrate. A good control of these layers is mandatory to ensure the reliability of the devices. In particular, heavily doped epitaxial layers of Si or SiGe are needed to reduce contacts resistivity. The objective of this thesis is to develop a characterization technique able to measure the dopants concentration and their chemical environment which determines whether they are electrically active or not. The work will consist in evaluating X-ray photoelectron spectroscopy (XPS) to quantify dopants such as As, P and B and identity they chemical state. The impact of growth parameters, particularly the temperature and the precursors, will be evaluated to obtain heavily doped thin films without morphological degradation while minimizing inactive dopants concentration. Methodological developments will be carried out with XPS to obtain a reproducible and in-line characterization of the doping. Experimental conditions and spectra post-treatments will be optimized. The PhD student will also do exploratory measurements using hard X-ray photoelectron spectroscopy (HAXPES). Other characterization techniques such as SIMS, FTIR and XRD as well as electrical measurements will complement the results.
Investigation of Degradation Modes of PV Modules for Medium Voltage Applications
To reduce energy losses and costs, photovoltaic power plants have seen their voltage levels increase, which should exceed the standard 1500 Vdc to reach up to 3000 Vdc soon. Even higher voltages, up to 9000 V, could offer very significant economic advantages.
The increase in voltage presents major scientific challenges. High voltages result in degradations grouped under the term Potential-Induced Degradation (PID), leading to significant power losses. These degradations can have several origins: a short circuit of the PN junction (PID-s), depolarization of the solar cell passivation layer (PID-p), or corrosion of the metallization (PID-c). The understanding of PID phenomena at medium voltage remains limited, and there is a lack of comprehensive studies on module materials, the interaction between PID and aging, and the behavior of PID in advanced cell technologies.
The proposed thesis aims to identify the material properties necessary for PV modules to withstand high voltages, combining experimental and simulation approaches.
The doctoral student will need to characterize PV materials (glass, encapsulants) under voltages up to 9000 V, evaluate the impact of aging on the properties of these materials, study the influence of environmental conditions on PID, model the electric field distribution in PV modules, and develop PID mitigation strategies. This research will be conducted in collaboration between G2Elab and CEA, combining expertise in material characterization under medium voltage and photovoltaic module manufacturing.
Nonlinear magnetization dynamics triggered by surface acoustic waves
RF signals are everywhere in today’s connected society. On one side, surface acoustic wave (SAW) devices built on piezo-electric substrates are widely used to distinguish between frequencies. While quite energy efficient, SAW devices mostly operate in narrowband applications and perform linear, frequency-conserving operations. On the other side, magnonic devices rely on the specific properties of spin-waves (SW) in ferromagnetic materials and are highly tunable and nonlinear, but suffer from significant insertion losses. Fortunately, magneto-elastic and magneto-rotation effects can couple the dynamics of magnetization in a thin ferromagnetic film deposited on top of a piezo-electric substrate to the one of its lattice. For instance, we have recently demonstrated that it is possible to excite the linear magnetization dynamics of a ferromagnetic CoFeB nanodisk thanks to SAW electrically actuated in the underlying LiNbO3 substrate [1].
The objective of this thesis will be to demonstrate that this can also be achieved in a nonlinear regime. For this, we will magnetize the ferromagnetic disk in the plane. In this configuration, the precession of magnetization is elliptical, which allows to excite parametrically spin-wave eigenmodes of the disk using an RF magnetic field parallel to the disk’s magnetization with a frequency close to twice the eigenfrequencies [2]. The originality here will be to replace the RF excitation field usually produced by an inductive antenna by the effective tickle and rolling fields associated to the magneto-elastic and magneto-rotation terms active when a SAW is excited in the substrate. These measurements will be performed on samples fabricated in collaboration with another laboratory (C2N) and thanks to a highly sensitive magnetic resonance force microscopy technique developed at SPEC. Micromagnetic simulations using Mumax3 will also be conducted to understand the SAW excitation threshold to be overcome to excite parametric modes in the disk.
This thesis will take place in the context of the recently funded project NELSON (« Non-Linear Surface acoustic wave platform enabled by spin wave hybridizatiON ») by the French ANR.
[1] R. Lopes Seeger et al., Phys. Rev. Lett. 134, 176704 (2025)
[2] T. Srivastava et al., Phys. Rev. Appl. 19, 064078 (2023)
Towards Efficient and Authenticated Private Set Intersection in Multi-Cloud Environments
Private Set Intersection (PSI) is a cryptographic protocol that allows two (or more) parties, each holding a set of items, to compute the intersection of their sets without revealing any items outside the intersection. That is, each party learns only which items they have in common, and nothing else about the other’s data. For example, two companies could collaborate to find out which customers they share without disclosing any customers that are unique to one company. PSI is a special case of secure multi-party computation focusing on set overlap; it has applications in private contact discovery, privacy-preserving data mining,..In this thesis, we study PSI improvements with fully holomorphic encryption.
In-situ Monitoring of RF Power Amplifier Circuits Aging for Eco-design and Extended Lifetime
The semiconductor industry, and more specifically the radio-frequency (RF) circuit sector, is facing critical challenges related to eco-design and eco-innovation. These challenges include the need to extend the lifetime of circuits while meeting the growing demands of emerging markets such as 5G and the future 6G. Among these circuits, power amplifiers (PA) play a central role, being both critical components in terms of energy efficiency and key targets for improving robustness against aging and enabling potential reuse.
In this context, in-situ aging monitoring of PAs appears to be a promising approach for developing innovative and sustainable solutions. This research topic is therefore fully aligned with eco-design strategies, leveraging advanced technological platforms such as current and future CMOS SOI technologies, while integrating industrial constraints through existing strategic collaborations with major partners of CEA Leti.
This thesis aims to design an innovative in-situ monitoring solution to evaluate and compensate for the aging of power amplifiers, thereby extending their lifetime through reuse and self-correction strategies. To achieve this, it will rely on methodologies and circuits specifically adapted to practical use cases. The ambition is thus to develop a new generation of robust and durable circuits, integrating intelligent aging management mechanisms. By adopting an eco-design approach, this work aims to address environmental challenges while enhancing the industrial competitiveness of CMOS SOI technologies.
Study of vibration effects on electrical cable diagnosis using reflectometry
This thesis focuses on the effect of vibrations on the diagnosis of electrical cables using reflectometry. Cable systems, which are present in many critical infrastructures such as aeronautics, railways, space systems, and nuclear facilities, are exposed to mechanical and environmental stresses that may lead to soft and intermittent faults. Under vibration, these faults may appear, disappear, or modify their electrical signature, making their detection particularly challenging.
One of the main challenges concerns No Fault Found situations, in which a fault observed during operation becomes non-reproducible once the vibration conditions disappear. Another important issue is the temporary masking of certain faults by vibrations, which may lead to false negatives during diagnosis and delay the detection of latent degradation.
The objective of this thesis is to better understand and model the electromechanical behavior of cable faults subjected to vibrational stress, in order to link vibration profiles, the mechanical and electrical evolution of the fault, and the signatures measured by reflectometry. The work will be based on experiments combining fast reflectometry and a high-speed camera, as well as on the development of models and analysis tools. Experimental and simulated data will then be used to improve the detection, characterization, and prediction of fault evolution, with a view to advanced diagnosis and predictive maintenance.
Enhanced thermal resistor nanomaterials development, based on amorphous-Si, for microbolometers
The aim of this thesis is to develop high-performance materials for the next generation of microbolometers, with a particular focus on increasing the thermal resistance of the supporting arms to enable smaller pixel pitches. Our approach aims to take advantage of the lower thermal conductivity of materials induced by controlled inhomogeneities at the nanoscale. For this purpose, we have already demonstrated the fabrication of nanocrystallized amorphous silicon (nc-aSi) thin films (few tens of nanometers thick) with promising thermal conductivity.
In the case of nc-aSi, a range of characterization techniques—including Raman spectroscopy, X-ray diffraction, transmission electron microscopy (TEM), and the 3? method for thermal conductivity measurements—will be employed to correlate deposition conditions, nanostructure and thermal transport properties, in order to identify strategies for reducing its thermal conductivity.
The knowledge gained from studying nc-aSi could be extended to other materials.
Ideally, combining thermal measurement analysis with theoretical conduction models will provide insight into the mechanisms of heat propagation in these nanocrystallized materials.
Finally, the technological integration potential of these materials within the microbolometer fabrication line will be evaluated, including the mechanical strength and the thermal robustness.
Understanding microstructural changes during heat treatment of iron-rich SmCo magnets
The magnetic properties of SmCo magnets (remanence and coercivity) are linked to their microstructure. The final microstructure develops after sintering during homogenization and ageing heat treatments. The optimum temperature and/or duration of these treatments depend on the magnet’s composition. One of the major areas of development for commercial Sm2Co17 magnets is to achieve both high magnetic performance and a reduction in critical materials (notably cobalt). This is achieved by substituting part of the Co with Fe, which also helps to reduce raw material costs. However, the literature shows that when the Fe content exceeds 20% by weight, the coercivity of the magnets is diminished.
The aim of the thesis will be to understand the role and sensitivity of the process parameters that govern the evolution of the microstructure within Fe-rich Sm2Co17 magnets and the resulting properties. These developments will be monitored through various characterization techniques (chemical analyses, magnetic measurements, SEM and TEM observations, etc.) carried out on samples taken at different stages of the process. The aim is to systematically monitor (for the first time for this type of magnet) the structural transformations (chemical segregation, changes in Sm content, presence of defects, oxygen contamination, etc.) that occur from the synthesis of the alloy through to the final magnet. These characterizations should lead to a description of the mechanisms underlying the formation of the expected microstructure. These mechanisms are activated during the various heat treatments, but the influence of the metallurgical and chemical state (for example, defect density and chemical inhomogeneity) inherited from previous stages of the process is still poorly understood and will need to be clarified.
Scalable Network Digital Twins through Adaptive Fidelity Management
Future communication systems such as 6G networks are evolving toward highly distributed, autonomous, and heterogeneous infrastructures integrating cloud-edge continuum architectures, Open RAN (O-RAN), massive IoT deployments, edge computing, and highly dynamic wireless environments.
These systems are expected to support demanding services such as mission-critical communications, industrial automation, autonomous mobility, and immersive applications, operating under highly dynamic traffic conditions, frequent topology changes, fluctuating resource availability, and stringent latency and reliability requirements.
Managing such systems through risk-free configuration, optimization, and evolution operations is becoming increasingly challenging. This is particularly true when performing real-time network optimization, operational what-if analysis, network troubleshooting, or planning network upgrades and extensions.
To address these challenges, recent research initiatives have investigated the application of the Digital Twin paradigm to communication networks, commonly referred to as Network Digital Twins (NDTs).
An NDT is a virtual representation of a communication network that remains sufficiently aligned with the physical infrastructure to reproduce its operational state and behavior, support predictive analysis, and evaluate hypothetical scenarios before applying decisions to the real system.
However, maintaining an accurate and temporally consistent NDT in large-scale and highly dynamic networks remains a major challenge.
Current NDTs predominantly rely on explicit synchronization mechanisms to maintain fidelity between the physical and virtual systems. Although recent works have introduced AI-assisted prediction mechanisms to reduce synchronization overhead, these approaches do not fully address the problem of dynamically adapting the fidelity of the NDT according to predictive uncertainty, information value, network dynamics, and operational requirements. Adaptive fidelity can be interpreted as a multi-resolution representation mechanism, where the NDT dynamically adjusts its observation granularity, synchronization overhead, and reconstruction accuracy according to information value, predictive uncertainty, network dynamics, and available resources. The main objective of this PhD thesis is to design, develop, and validate an Adaptive Fidelity Management framework enabling scalable and resource-efficient Network Digital Twins for future communication systems.