Clément Jacquemin

Enedis is the main electricity distribution network operator in France, with over 1.3 million kilometers of medium and low-voltage lines. As a major player in the modernization of energy infrastructures, Enedis deploys smart grid technologies and plays a central role in the energy transition.

• Technical referent for the IEC 61850 standard, responsible for the design, integration, and validation of the protocol within new communication-enabled electrical substation architectures. Use of open-source and proprietary solutions (Triangle Microworks, Omicron) to ensure compliance with standards, interoperability between manufacturers, and equipment robustness.

• Research and development on virtualization technologies for future MV/LV substations, including the study of real-time containerization and orchestration to design more modular, scalable, and resilient components.

• Design and development of embedded systems in C, C++, Python, and MicroEJ, on various platforms integrating real-time operating systems (RTOS) and embedded Linux environments.

• Prototyping and implementation of flexibility solutions to strengthen grid stability, optimize energy flows, and facilitate the integration of decentralized energy production (RES, self-consumption, etc.).

• Collaboration with internal experts and external partners (electrical systems, cybersecurity, industrial software) to bring innovations from the lab to field deployment.

Technologies: C, C++, Python, Embedded Linux, RTOS, IEC 61850, DLMS/COSEM, IEC 62056, Modbus, G1/G3 PLC (CPL), MicroEJ, Smart Grid, Virtualization, Containerization, Edge Computing, Real-time Systems, Electrical Flexibility, MV/LV Substations

Cephalgo is a French startup specializing in the development of portable medical devices for continuous mood monitoring. By leveraging EEG signals, their discreet sensors, compatible with glasses, provide real-time emotional data, allowing both individuals and mental health professionals to manage and understand emotional well-being.

• Design of the system architecture and development of embedded software on the Nordic nRF52 SDK, ensuring low power consumption and robustness compliant with the requirements of portable medical devices.

• Design and implementation of firmware modules for SPI Flash memory, analog signal acquisition (SAADC), cryptographic services, and communication via Bluetooth Low Energy (BLE).

• Application of regulatory requirements for medical devices, including standards EC 80601-2-26 (ambulatory EEG systems) and CE.

• Collaboration with hardware and biomedical signal processing teams to integrate real-time EEG signal acquisition and pre-processing.

• Design and development of a Python tool suite for validation, data acquisition, and communication with devices during testing and R&D phases.

Technologies: Embedded C, Python, Nordic nRF52, Bluetooth Low Energy (BLE), ARM Cortex-M, SPI Flash, SAADC, UART, GPIO, Secure Firmware, EEG signal acquisition, Medical Device Certification (IEC 80601-2-26), Python, Medical-grade Embedded Systems, Low-power Embedded Systems

Inarix is a French startup specializing in agri-tech, using artificial intelligence and computer vision to analyze the quality of agricultural products such as grains, seeds, or vegetables, directly in the field or at processing sites. Their solutions aim to provide rapid, reliable, and easily deployable quality control across the entire agricultural supply chain.

• Design and development of industrial systems based on NVIDIA Jetson platforms, combining embedded Linux, AI inference, and computer vision to detect and analyze various types of crops (seeds, cereals, vegetables, potatoes, etc.) in real time.

• Creation and annotation of image datasets for training custom DetectNet models (SSD-MobileNet and SSD-Inception) adapted for visual inspection of agricultural samples; optimization of inference pipelines for real-time performance on Jetson embedded hardware.

• Design of industrial conveyor-based sampling systems for collecting image datasets from various cameras and optics, with the goal of training models robust to diverse visual conditions.

Technologies: C/C++, Embedded Linux, NVIDIA Jetson (Nano/Xavier), DetectNet, SSD-Mobilenet, SSD-Inception, TensorRT, Computer Vision, Deep Learning, Dataset Engineering, Industrial Prototyping, Python, Camera Sensor Integration, Sony Camera Remote SDK