Model-Based Systems Engineering approach for the automation and optimization of qubit control architecture exploration
Designing the system architecture to control millions of qubits in a quantum computer is a complex challenge, driven by strict constraints spanning heterogeneous technical domains (microelectronics, quantum information, thermics). Addressing this complexity requires dedicated formalisms and tools to enable automated design and optimization. Where traditional approaches remain siloed, Model-Based Systems Engineering (MBSE) and the SysML v2 standard modeling language provide a unique integration framework for analyzing and exploring complex multi-physics design spaces.
To capture these architectures within SysML v2 models, this research aims to adapt this modeling language to the quantum domain by developing dedicated extensions (libraries and metadata).
Based on these models, this work will explore how reference quantum or hybrid algorithms impact these architectures, particularly through resource estimation for these applications and the applied error-correction mechanisms.
The PhD thesis intends to develop a methodology for automating and optimizing architectural exploration. To this end, it will investigate the adaptation of operational research algorithms for design space exploration, incorporating sensitivity analyses, constraint relaxation heuristics, and physical uncertainties.
This research aims to identify the most influential parameters and provide new insights capable of guiding efforts toward the most promising R&D avenues.
The thesis will be carried out at CEA Grenoble.
Design and Optimization of a Compact MIMO Antenna for FR3-Band Communications
The evolution of cellular wireless communication networks toward distributed MIMO infrastructures requires a rethinking of antenna architectures to adapt them to advanced signal-processing strategies and to ensure optimized, continuous, and robust connectivity. In this context, the use of new frequency bands around 7 GHz opens new opportunities for the development of compact antenna architectures capable of increasing antenna directivity, generating multiple beams, and electronically steering them in different angular directions, both at the user-terminal level and at the radio access points that are expected to become increasingly dense in future networks.
The objective of this PhD thesis is to develop a compact multi-port antenna solution adapted to a hybrid analog-digital beamforming strategy, enabling the transmission of independent data streams while optimizing the spectral and energy efficiency of the communication link. The proposed approach will rely on an original multi-beamforming concept combining superdirectivity with a high-efficiency radiating surface.
The development of such a solution raises several scientific and technological challenges that must be addressed jointly with the development of the required analysis, modelling, and design tools. Particular attention will be devoted to the control of electromagnetic coupling between the different ports of the MIMO antenna, as well as to the energy consumption of the complete electronic system required for multi-beam generation and steering.
The research work will be organized around the following activities:
- State-of-the-art analysis of scientific and technological developments related to distributed MIMO networks, compact antennas, superdirectivity, and hybrid and multi-beamforming techniques.
- Development of numerical tools for antenna analysis, design, and optimization, including approaches based on spherical-wave decomposition and characteristic mode theory.
- Electromagnetic simulation and evaluation of antenna architectures for multi-beam generation and beam steering. This activity will investigate, in particular, holographic-volume architectures based on the concept of superdirectivity, canonical unit cells, and electronic beam-control architectures. Particular attention will be paid to controlling the electromagnetic coupling between the different antenna ports.
- Design of a compact electronically steerable multi-beam antenna system, including the design of the radiating cells, the development of a low-power RF control architecture, the optimization of the control electronics, and prototype fabrication.
Fabrication and experimental characterization of the electronically steerable multi-beam antenna system, including the evaluation of its radiation performance, port-to-port coupling, directivity, angular scanning capability, and power consumption.
The research will be conducted within the framework of the EXCELL-FR3 project, funded by the PEPR Future Networks program under France 2030.
Tuning the Electronic Structure of Tungsten Oxide Thin Films by Combinatorial Deposition: Impact of Intrinsic and Extrinsic Defects on Electrochemical Properties (DETOX Project)
We are offering a fully funded PhD position within the DETOX project, a collaborative research project involving CEA-TECH, CEA-INSTN, the Bordeaux Institute of Condensed Matter Chemistry (ICMCB), and FCSEL-LaRFIS at Polytechnique Montréal.
The DETOX project is dedicated to accelerated materials discovery, with a particular emphasis on electrochemical materials for electrochromic and photoelectrocatalytic applications. These two functionalities share a common redox origin: electrochromic materials reversibly modulate their optical properties under an applied voltage, while photoelectrocatalytic materials promote hydrogen production through solar-driven water splitting. Hence, understanding and controlling the relationship between defects, electronic structure, and electrochemical performance is a key scientific challenge.
The PhD project will focus on the synthesis of tungsten oxide (WO3)-based thin films using combinatorial magnetron sputtering. Tuning deposition parameters—including target composition, working pressure, and reactive atmosphere—the candidate will investigate how intrinsic and extrinsic defects influence the structural, morphological, electronic, and electrochemical properties of the deposited materials.
Combinatorial thin-film deposition provides a powerful high-throughput approach for rapid exploration of complex materials parameter spaces. Coupled with artificial intelligence and machine learning tools for data analysis and materials prediction, this strategy offers a significant acceleration in the discovery and optimization of functional materials.
The successful candidate will join a multidisciplinary consortium having complementary expertise in thin-film deposition, electrochemistry, materials characterization, and data-driven materials science. Throughout the project, the student will work closely with researchers from the four partner institutions, benefiting from their active supervision and scientific support.
The PhD will be carried out within the DIADEM Priority Research Programme and Equipment (PEPR), a national initiative coordinated by CNRS and CEA which aims to accelerate the discovery of innovative materials through artificial intelligence.
The research will be held primarily in Bordeaux, France at two partners laboratories:
- CEA Tech Materials Screening Platform (PRTT Nouvelle-Aquitaine)
- Bordeaux Institute of Condensed Matter Chemistry (ICMCB)
The project will also include regular research visits to CEA-INSTN in Saclay and an international research stay of 9 to 12 months at Polytechnique Montréal (Canada), providing the candidate a valuable international research experience.
3D Bio-Based mRNA-Enriched Materials for Bone Regeneration: Optimizing Osteogenic Differentiation Using AI-Based Methods
This PhD project aims to develop bioresorbable scaffolds functionalized with mRNA using a polycation, poly(L-lysine), and hyaluronic acid, arranged in different structural architectures.
The originality of this approach lies in two key aspects. First, these biomaterials will incorporate mRNA sequences encapsulated within nanoparticles, encoding proteins that play a crucial role in bone regeneration, such as VEGF (Vascular Endothelial Growth Factor) and BMP (Bone Morphogenetic Proteins). Second, the project will investigate how the three-dimensional organization of the polymer network influences the biological performance of the scaffold.
The overall objective is to transform cells located at the injury site into localized therapeutic protein factories through in situ transfection, enabling sustained, localized production of the therapeutic proteins at physiological concentrations.
Artificial intelligence (AI) approaches will be used as computer-aided design tools to analyze and exploit the experimental data. These methods will guide the optimization of scaffold design—including composition, architecture, and mRNA loading—to maximize osteogenic differentiation while reducing the number of experimental iterations required.
This work is part of an interdisciplinary and collaborative research project. The PhD candidate will also contribute to data analysis, the writing of scientific publications, and the dissemination of research findings at national and international conferences.