Definition and implementation of metrics for software obsolescence measurement
The environmental impact of digital technology has become a major concern, with a measurable and growing environmental footprint (particularly carbon). A significant part of this impact comes from the manufacture of equipment, which is often replaced prematurely, partly due to software-induced obsolescence. “Programs slow down faster than hardware improves” is how N. Wirth's law is formulated. Every computer or smartphone user experiences this during the many software updates, until the computer or phone can no longer support the demands of the applications.
Unfortunately, this law has never been formalized or measured experimentally; that is the objective of this project.
More specifically, the objective is to develop metrics on the evolution of the operational complexity of software across its different versions. These metrics can then be used in software workshops and possibly meet regulatory requirements: “my software must not increase in complexity by more than 7% per year” in order to increase the lifespan of hardware, which accounts for the majority of the environmental footprint of digital technology.
In practice, this will involve developing a methodology for tools of increasing complexity, using usage scenarios to measure operational complexity.
This method will be applied to one or more use cases, such as an open-source word processing scenario (LibreOffice) and a web-based scenario.
Integrating dynamic CRDTs replicas
Existing modeling frameworks have limited collaboration capabilities. Collaboration at the model level is one of the top desired features. However, most solutions rely on cloud-based and centralized databases as their technological solution. While these solutions ease collaboration among connected partners by employing concurrency control techniques, they do not support disconnected collaboration scenarios, which is an important feature for designing local-first software. This situation presents a significant compromise: utilizing cloud-based solutions with loss of data ownership control versus adopting separate instances without collaborative capabilities.
The objective of this postdoctoral project is to contribute to and extend an existing local-first Model-Based Systems Engineering (MBSE) framework, built upon specialized Conflict-free Replicated Data Types (CRDTs). The goal is to enable real-time collaboration through modeling-specific CRDTs. The proposed approach involves extending a middleware communication layer utilizing CRDTs to
seamlessly synchronize distributed, offline-capable engineering models.
The postdoctoral researcher will conduct a state-of-the-art review of communication and group membership approaches in P2P environments. One major issue to be taken into account is the entry and exit of members in a group, so the CRDT state is always
coherent. The components will be integrated into our CRDT and modeling framework.
Thermal properties of 3D aluminum nitride structures for electronic packaging
The 12-month postdoctoral fellowship is part of the overall 3DNAMIC project, funded by the Occitanie region and involving the Materials platform of the DRTDOCC department and the Laplace laboratory. A thesis began in December 2024 aimed at “the study and characterization of 3D aluminum nitride ceramics for the thermal packaging and management of electronic components.”
The postdoc is scheduled to begin at approximately in September 2026, with the following main objectives:
Objective 1: Perform a comparative analysis of the thermal properties of ceramics produced by AF elements and on model structures using different materials available in the CEA materials platform.
Objective 3: Propose, qualify, and validate, numerically and then experimentally, heat dissipation structures for ceramics obtained by FA as part of the 3DNAMIC project.
Analysis of Gas Effluents for More Eco-Responsible Plasma Etching Processes
Traditionally used fluorinated gases, such as CF4 and C4F8, exhibit extremely high Global Warming Potentials (GWPs), significantly contributing to climate change. To address these environmental challenges, the project aims to promote the use of alternative low-GWP gases in combination with efficient exhaust abatement systems at the reactor outlet, while maintaining high-performance plasma etching processes. In this context, the postdoctoral researcher will be responsible for the analysis and characterization of gaseous species in an industrial plasma etching reactor using mass spectrometry. These measurements will be compared with the gas effluent at the outlet of the pumping and abatement systems. The main objectives are (i) to quantify the Destruction Removal Efficiency (DRE) of high and low GWP fluorocarbon gases during plasma processing and within the pumping and abatement stages, and (ii) to identify and propose innovative, environmentally responsible alternatives to minimize the release of high-GWP gaseous effluents.
Tools and diagnostic methods for the reuse of electronic components
The Autonomy and Sensor Integration Laboratory (LAIC) at CEA-Leti has the primary mission of developing sensor systems for the digitalization of systems. The team's activities are at the interface of hardware (electronics, optronics, semiconductors), software (artificial intelligence, signal processing), and systems (electronic architecture, mechatronics, multiphysics modeling).
In a context of exponential growth in electronics and scarcity of resources, the reuse of electronic components from end-of-life systems represents a promising avenue to limit environmental impact and support the development of a circular economy. The objective of this project is to develop an advanced diagnostic methodology to assess the health status of electronic components, particularly power components, to reintegrate them into a less constraining second-life cycle.
The postdoctoral researcher will be tasked with developing a comprehensive approach to evaluate the reuse potential of electronic components, with the aim of reintroducing them into second-life cycles. This will include:
- Identifying relevant health indicators to monitor the performance evolution of components (e.g., MOSFETs, IGBTs, capacitors, etc.);
- Setting up test benches and sensors adapted to measure electrical, thermal, or mechanical parameters, with the goal of detecting signs of aging;
- Analyzing degradation modes through experimental tests and failure models;
- Developing algorithms for predicting the remaining useful life (RUL) adapted to different usage scenarios;
- Contributing to scientific publications, the valorization of results, and collaboration with project partners.
Design and development of a modular high-side test bench for application validation of Grand Gap components
Wide bandgap transistors (GaN, SiC) play a key role in power electronics, but their industrial integration remains hampered by implementation difficulties. The high-side component, within a bridge arm structure, is particularly sensitive to voltage and current transients, which are highly dependent on routing, topology, and switching modes (ZVS, ZCS). Its floating nature makes measurements complex and can disrupt switching during application testing. A methodology adapted to fast transients was developed during a thesis, resulting in a patented test bench for characterizing low-side components. The subject of the postdoctoral research presented here aims to adapt this methodology to high-side components, which are more complex to drive and measure, in order to characterize and model aging due to gate transients under realistic conditions. The test bench will enable the generation of reproducible stress profiles on low-side and high-side components, and the precise measurement of key parameters such as threshold voltage and dynamic instabilities. To achieve these objectives, a new bench will be designed, incorporating specific control and measurement systems, with a view to application testing and targeted aging tests.
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Diamond-based electrochemical sensors for monitoring water pollution in urban environments
This postdoctoral position is offered by CEA List as part of the European UrbaQuantum project ("A novel, Integrated Approach to Urban Water Quality Monitoring, Management and Valorisation"), part of the HORIZON-CL6-2024-ZEROPOLLUTION-02 call for projects. The main objective of this project is to develop, in response to climate change, sensors, models, and protocols for better management of the water cycle in urban environments.
At the Sensors and Instrumentation for Measurement Laboratory (LCIM)of CEA List the postdoctoral fellow will contribute to the development of electrochemical sensors based on synthetic diamond and associated measurement protocols for the detection of pollutants such as pharmaceuticals, heavy metals, PFAS, and pesticides. These sensors will be miniaturized and integrated into a microfluidic cell, in partnership with CEA-Leti, then tested under real-world field conditions.
High-performance computing using CMOS technology at cryogenic temperature
Advances in materials, transistor architectures, and lithography technologies have enabled exponential growth in the performance and energy efficiency of integrated circuits. New research directions, including operation at cryogenic temperatures, could lead to further progress. Cryogenic electronics, essential for manipulating qubits at very low temperatures, is rapidly developing. Processors operating at 4.2 K using 1.4 zJ per operation have been proposed, based on superconducting electronics. Another approach involves creating very fast sequential processors using specific technologies and low temperatures, reducing energy dissipation but requiring cooling. At low temperatures, the performance of advanced CMOS transistors increases, allowing operation at lower voltages and higher operating frequencies. This could improve the sequential efficiency of computers and simplify the parallelization of software code. However, materials and component architectures need to be rethought to maximize the benefits of low temperatures. The post-doctoral project aims to determine whether cryogenic temperatures offer sufficient performance gains for CMOS or should be viewed as a catalyst for new high-performance computing technologies. The goal is particularly to assess the increase in processing speed with conventional silicon components at low temperatures, integrating measurements and simulations.
Digital correction of the health status of an electrical network
Cable faults are generally detected when communication is interrupted, resulting in significant repair costs and downtime. Additionally, data integrity becomes a major concern due to the increased threats of attacks and intrusions on electrical networks, which can disrupt communication. Being able to distinguish between disruptions caused by the degradation of the physical layer of an electrical network and an ongoing attack on the energy network will help guide decision-making regarding corrective operations, particularly network reconfiguration and predictive maintenance, to ensure network resilience. This study proposes to investigate the relationship between incipient faults in cables and their impact on data integrity in the context of Power Line Communication (PLC). The work will be based on deploying instrumentation using electrical reflectometry, combining distributed sensors and AI algorithms for online diagnosis of incipient faults in electrical networks. In the presence of certain faults, advanced AI methods will be applied to correct the state of the health of the electrical network's physical layer, thereby ensuring its reliability.
Advanced reconstruction methods for cryo-electron tomography of biological samples
Cryo-electron tomography (CET) is a powerful technique for the 3D structural analysis of biological samples in their near-native state. CET has seen remarkable advances in instrumentation in the last decade but the classical weighted back-projection (WBP) remains by far the standard CET reconstruction method. Due to radiation damage and the limited tilt range within the microscope, WBP reconstructions suffer from low contrast and elongation artifacts, known as ‘missing wedge’ (MW) artifacts. Recently, there has been a revival of interest in iterative approaches to improve the quality and hence the interpretability of the CET data.
In this project, we propose to go beyond the state-of-the-art in CET by (1) applying curvelet- and shearlet-based compressed sensing (CS) algorithms, and (2) exploring deep learning (DL) strategies with the aim to denoise et correct for the MW artifacts. These approaches have the potential to improve the resolution of the CET reconstructions and facilitate the segmentation and sub-tomogram averaging tasks.
The candidate will conduct a comparative study of iterative algorithms used in life science, and CS and DL approaches optimized in this project for thin curved structures.