General information
Organisation
The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :
• defence and security,
• nuclear energy (fission and fusion),
• technological research for industry,
• fundamental research in the physical sciences and life sciences.
Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.
The CEA is established in ten centers spread throughout France
Reference
2025-37213
Description de l'unité
CEA‑Leti works every day to link micro and nanotechnology research with industrial and consumer applications, all with the aim of improving people's quality of life. Located in Grenoble, Leti employs more than 1,800 top researchers and has offices in the United States and Japan.
Within the institute, the Service of Sensor Systems and Electronics for Energy (SSCE) runs a Laboratory of Sensor Signals and Systems (LSSC). The lab focuses on fusing sensor signals, exploiting multimodality through studies in signal processing, information processing, and embedded algorithms. These efforts are especially aimed at context‑capture functions and interaction with the environment from mobile sensor systems.
Position description
Category
Mathematics, information, scientific, software
Contract
Internship
Job title
Machine learning for circuit parameters optimisation and RF signals analysis
Subject
A laboratory in the Systems Department at CEA Leti has developed a circuit that resembles a very low-power time-domain spectrum analyzer for radio-frequency (RF) signals. It can be used for various tasks:
- Detecting an RF signal,
- Classifying WiFi/Bluetooth signals,
- Detecting/identifying drones, etc.
To achieve this, the circuit must be properly configured and paired with a dedicated machine learning algorithm, such as a classifier. Determining the optimal parameters for the circuit, selecting the appropriate algorithm for the task, and training it on data are open questions that we aim to address during this internship.
Contract duration (months)
6 months
Job description
The mentioned circuit is the result of a thesis work, with the report available here:
"Intelligent RF System for Ultra Low Power Spectrum Sensing with Machine Learning."
Chapter 2 motivates its design, while Chapters 3 and 5 are of direct interest for the internship. Currently, the circuit parameters are set by human expertise, and the algorithm is chosen a posteriori through optimization on data (Chapter 5). This process must be repeated for each new task the circuit can be used for.
The goal is to develop a learning algorithm that jointly optimizes the circuit parameters with the mathematical model required to perform the task (detection, classification, etc.) to achieve a more optimal solution (better performance at equal complexity or vice versa). This more generic learning approach should also facilitate the reuse of the circuit for other applications.
We have a relatively large database of RF signals for various use cases (WiFi, Bluetooth, drones, etc.) and a relatively simple Python simulator of the circuit for quick familiarization.
There is no clearly identified solution in the literature for this problem. As a first step, a simple solution to serve as a benchmark will be to select parameter value samples according to an experimental design and then train the learning model for each sample to determine the most performant one. For the next steps, a considered approach would be to implement reinforcement learning to enable automatic selection of the best circuit parameters and the coefficients of the learning model.
Successful results could be valorized through a scientific publication.
Methods / Means
RF signal database, circuit simulator, python
Applicant Profile
Final-year engineering student or master's student with a specialization in machine learning, optimization, and signal processing; knowledge in radio frequencies and/or reinforcement learning is an asset.
The candidate must demonstrate curiosity, autonomy, and perseverance in their work, as this subject is an innovative research question that is not clearly addressed in the literature.
Position location
Site
Grenoble
Job location
France, Auvergne-Rhône-Alpes, Isère (38)
Location
Grenoble
Candidate criteria
Languages
- French (Fluent)
- English (Fluent)
Prepared diploma
Bac+5 - Master 2
Recommended training
Machine learning, signal processing
PhD opportunity
Non
Requester
Position start date
02/02/2026
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