Sustainable development of digital circuits and systems: Taking planetary boundaries into account
Technological developments in the electronics sector are experiencing rapid growth, accompanied by increasing interest in accounting for their environmental impacts. However, current approaches remain largely focused on relative impact reductions (energy efficiency, resource optimization), without ensuring compatibility with planetary boundaries. In this context, the concept of absolute sustainability emerges as an essential framework for guiding future developments of electronic systems.
This PhD thesis addresses several major scientific challenges: how can carrying capacities and sharing principles (core concepts of absolute sustainability) be identified for the electronics sector and consistently translated down to the levels of digital systems and integrated circuits? How can planetary boundaries be concretely integrated into the design of systems and circuits?
The main objective of the thesis is to move from a logic of relative environmental impact reduction toward designs that are compatible with planetary boundaries. It aims to define socio-technical scenarios to identify sharing principles, to conduct the first absolute life cycle assessment of a digital system, and to propose the first design of a circuit based on absolute limits, paving the way for sustainable development in electronics.
Controlling the composition and microstructure to achieve high magnetic performance in 1–12 rare earth-poor magnets
Permanent magnets based on rare earth elements (REEs), particularly neodymium-iron-boron (Nd-Fe-B) magnets, are strategically important for the development of more efficient motors and generators (electric vehicles, wind turbines). However, REEs, particularly Nd, are critical materials, with a high risk of supply disruption in the coming years. The growing demand for high-performance magnets requires the development of new types of magnets with reduce RE content. Iron-rich compounds, such as Sm-Fe12 (commonly known as phase 1-12), have very interesting intrinsic magnetic properties and are considered the best alternative to NdFeB magnets, allowing for a TR saving of around 35% by weight. However, achieving sufficient magnetic performance (remanence > 1 T and coercivity > 800 kA/m) depends on obtaining a suitable microstructure and remains the main challenge in the development of Sm-Fe12 magnets.
The aim of the thesis is therefore to improve the magnetic performance of this new family of magnets, in particular by controlling the composition and distribution of phases at grain boundaries. The doctoral work will combine an advanced experimental approach (development of Sm-Fe12 alloys, characterization of equilibrium phases, magnet manufacturing, magnetic characterization) with knowledge of phase diagrams to define compositions and optimal manufacturing conditions to achieve the targeted magnetic performances.
Model-Driven DevOps for Cloud Orchestration : Bridging Design-Time and Runtime Guarantees
Model-Driven Engineering (MDE) has traditionally relied on a clear separation between design and runtime, but this boundary no longer holds in today's cloud-native and edge environments, where infrastructures are heterogeneous, dynamic, and continuously evolving. Assumptions validated at design time may become invalid during execution, and modern orchestration platforms such as Kubernetes or OpenStack, while effective, remain weakly connected to architectural modeling environments. This results in a structural gap between architectural specification and actual operational behavior. To bridge this gap, this thesis proposes to develop a formal modeling framework for placement constraints across heterogeneous orchestration platforms, ensuring continuity between design-time validation and runtime guarantees. This framework would elevate placement constraints — resource locality, affinity, network latency, security isolation, and quality-of-service objectives — to first-class modeling constructs. At design time, it would enable static feasibility analysis and automated generation of deployment artifacts; at runtime, it would ensure continuous compliance monitoring and adaptive reconfiguration in response to violations. Expected contributions include a formal modeling language, bidirectional transformations between design-time models and runtime representations, and integration with Papyrus-based tooling. The ultimate goal is to ensure that architectural intent remains consistent and verifiable throughout the entire system lifecycle, from initial design through to production operation.
Non-invasively exploring the cerebellum microstructure with magnetic resonance
To better diagnose and monitor brain diseases, we need “non-invasive biopsies” to access the tissue cell-type composition and state without opening the skull. Magnetic resonance imaging (MRI) research efforts attempt to tackle the challenge but often lack cellular specificity because of the ubiquitous nature of water. Diffusion-weighted magnetic resonance imaging (dMRS) measures diffusion of intracellular and partly cell specific molecules in a region of interest, and forms a solid basis for resolving cell-types non-invasively. Among challenges, resolving signal contributions from the different cerebellar neurons could help monitor and understand neurodevelopmental and ataxic disorders. The cerebellum is a brain region representing 10% of the brain volume but containing more than half of the brain neurons, with the very large and complex Purkinje cells and the very small and round granule cells, both having very different functions and metabolism. The PhD project aims to disentangle these cells with complementary strategies: a classical dMRS approach and a quantum dMRS approach confronted to the state-of-the-art microstructure MRI methods.
Topologically Isolated Mode Acoustic Resonators
Timing is a key function in electronic circuits. Beyond on-chip signals synchronization, it also allows the synchronization of wireless data transmissions. Accurate time references require stable frequency sources, which also benefit to sensor applications. The gold standard for time or frequency generation is still quartz resonators, which are however bulky and difficult to miniaturize. Research is therefore still ongoing to provide high quality factor (> 10,000) resonators, ideally capable of operating at frequencies of several GHz. A key to reach such high quality factors is to confine strongly the mechanical vibration of micro-size structures in order to make them insensitive to external perturbations. Recently, the field of topological acoustics has demonstrated the capability to confine elastic waves in very small volumes concentrated at the interface between periodic structure, and to provide extremely high quality factor resonances.
This PhD position focuses on exploiting topologically protected modes in piezoelectric microstructures to provide next generations of high quality factor resonators, which may be used in oscillators or even filter circuits. Leveraging the know-how of CEA Leti in the design and fabrication of such components, the PhD will be part of an international collaboration with well established academic laboratories (Politecnico di Milano, Imperial College FEMTO-ST Institute) and industrial partners.
The candidate will model and design structures supporting topologically protected modes, combinining finite element simulations with simplified numerical approaches which reduce computation times. He will follow the fabrication of demonstrators in collaboration with the process integration teams in the CEA Leti clean rooms, and carry on measurements of the proposed resonators.
Distributed multimodal learning for cooperative acoustic source localization and classification
In many complex environments, such as industrial sites, disaster-stricken buildings, or public spaces, it is necessary to automatically detect and localize sound events (falls, alarms, voices, mechanical failures). Mobile platforms equipped with cameras and microphones represent a promising solution, but a single platform remains limited: its microphone array provides an approximate direction towards the source but not a precise position in space, and its camera may be obstructed. This thesis proposes to study how a network of mobile platform, each carrying a calibrated audio-visual unit, can collaborate to localize and classify such events in 3D. Each platform analyses its own audio-visual observations and shares an estimate of the source direction with its neighbours; the network then combines these estimates to reconstruct the position of the event and identify it. The expected outcomes are a cooperative localization system that is robust to occlusions and partial platform failures.
Potential synergy between NH3 and NaBH4 for improved H2 density and enhanced safety
The thesis focuses on the study of the hybrid ammonia–sodium borohydride system (NH3–NaBH4) as an innovative chemical energy carrier. The objective is to investigate the combination of ammonia (NH3), recognised for its high hydrogen density and mature industrial infrastructure, with sodium borohydride (NaBH4), a high-capacity chemical hydrogen storage material, in order to overcome certain limitations associated with each vector when considered separately.
The proposed work specifically addresses the safer storage and transport of ammonia through its coupling with sodium borohydride, enabling a reduction in vapour pressure (compared to 8.88 bar at 21 °C for liquid ammonia) and less restrictive operating conditions. In parallel, the thesis aims to improve the stability (relative to the H2O–NaBH4 system) and operability of sodium borohydride which, when combined with ammonia molecules (acting as inert species), forms stable liquid or viscous phases that are potentially pumpable, thereby facilitating integration into energy-related processes.
The fundamental goal of the thesis is to understand the physicochemical mechanisms governing this hybrid system, particularly the role of dihydrogen interactions between the N–H bonds of ammonia and the B–H bonds of borohydride, and their influence on stability, reactivity, transport properties, and hydrogen release pathways (thermal and/or hydrolytic).
Beyond its storage function, the thesis also explores the potential of the NH3–NaBH4 system as a novel hybrid material with high gravimetric and volumetric hydrogen capacity, while considering realistic operational constraints relevant to energy applications in a dual-use context. At this stage, exhaustive optimisation is not the primary objective.
Intelligent control and optimization of DC microgrids using digital twins in real-time simulation
This thesis addresses the challenge of decarbonizing industrial and territorial systems by proposing a transition to direct current (DC) microgrids controlled by a Digital Twin. Faced with the saturation of alternating current (AC) grids due to the growth of photovoltaics, energy storage, and electric mobility, DC allows for a reduction in conversion losses (5 to 15%), improved flexibility, and a simplification of the electrical architecture.
The project is based on the development of a high-fidelity Digital Twin synchronized in real-time simulation. More than just a monitoring tool, it acts as a proactive decision-making system integrating advanced optimization algorithms, such as artificial intelligence and predictive control. It anticipates voltage instabilities, which are particularly critical in low-inertia DC grids, and continuously optimizes power flows to maximize self-consumption while preserving battery life.
Experimental validation relies on a Hardware-in-the-Loop approach within the CEA-Liten/G2Elab ecosystem, integrating physical converters. This methodology guarantees robustness, security, and resilience before any real-world deployment.
The expected outcomes are scientific (stability and real-time modeling), operational (provision of technical guides and decision-making tools), and strategic (strengthening French technological sovereignty in Smart Grids and accelerating the 2050 carbon neutrality trajectory advocated by ADEME).
High-Endurance Chalcogenide Memories for Next-Generation AI
Discover a unique phd opportunity where you will dive into the heart of innovation in memory technologies. You will develop strong expertise in areas such as electrical characterization and the understanding of degradation phenomena in chalcogenide-based memories.
By joining our multidisciplinary teams, you will play a key role in studying and improving the endurance of Phase-Change Memory (PCM) and Threshold Change Memory (TCM) devices—two promising technologies for high-performance artificial intelligence applications. You will take part in innovative projects combining scientific rigor and applied research on nanoscale devices, working closely with another CEA PhD student who conducts advanced physico-chemical analyses (TEM) to investigate degradation mechanisms.
You will have the opportunity to contribute actively to tasks such as:
Electrical characterization of PCM and TCM devices to analyze cycling-induced degradation
Development and evaluation of innovative programming protocols to extend endurance limits
Proposing solutions to improve the reliability and performance of next-generation memories
Regular collaboration and discussion with the CEA PhD student to interpret TEM results and draw conclusions about degradation mechanisms
Ultra-low frequency wireless power transmission for sensor node charging
Wireless power transfer (WPT) technologies are rapidly expanding, particularly for wireless charging of everyday electronic devices and for powering wireless communicating sensor nodes. However, their transmission ranges remain limited, and the high operating frequencies typically used prevent energy transfer in the presence of, or through, conductive media (such as metallic barriers or seawater). This constraint significantly limits their adoption in complex environments (industrial, biomedical, etc.).The ultra-low-frequency technology investigated in our laboratory is based on an electromechanical receiver system comprising a coil and a magnet set into motion by a remotely generated magnetic field. The objective of this PhD project is to propose and develop novel ultra-low-frequency concepts to increase transmission range while maintaining sufficient power density for supplying sensor systems. The work will therefore involve studying, designing, optimizing, and experimentally validating the performance of new topologies (emitter field shaping, receiver geometries and materials, etc.). The candidate will develop analytical and numerical models to identify key system parameters and compare performance with the state of the art (range, power density, sensitivity to orientation). In addition, the candidate will propose, design, and experimentally evaluate innovative energy conversion electronics, on the transmitter and/or receiver side, to assess their impact on the overall system performance. A joint optimization of the electromechanical system and its associated power electronics will ultimately lead to the realization of a high-performance wireless power transfer system. A multidisciplinary profile with a strong orientation toward physics and mechatronics is sought for this PhD project. In addition to solid theoretical foundations, the PhD candidate must demonstrate the ability to work effectively in a team environment as well as a strong aptitude for experimental work. The PhD candidate will be integrated into the Systems Department of CEA-Leti, within a team of researchers with strong expertise in the development and optimization of electronic and mechatronic systems, combining innovative solutions for energy harvesting, wireless power transfer, low-power electronics, and sensor integration aimed at the development of autonomous systems.