Electromagnetic Signature Modeling and AI for Radar Object Recognition
Develop advanced near-field radar electromagnetic models and physics-assisted AI algorithms for next-generation sensing systems.
Best for:Best for Master's graduates in engineering or signal processing with a strong background in electromagnetics and an interest in AI.
Overview
This doctoral position offers a compelling opportunity to conduct interdisciplinary research at the intersection of electromagnetics, numerical simulation, and artificial intelligence. Based at the Antenna & Propagation Laboratory at CEA-LETI in Grenoble, France, the selected candidate will focus on physics-based modeling of near-field radar backscattering for mmWave and THz systems. The project involves validating models through measurements, integrating them into ray-tracing tools, and leveraging AI for object recognition and 3D Semantic Radio SLAM frameworks. The research also includes a planned collaborative stint with the University of Bologna in Italy.
What you'll do
- Develop advanced electromagnetic models of near-field radar backscattering for mmWave and THz frequencies
- Account for near-field effects, multistatic configurations, and material properties in target signatures
- Validate generated models through dedicated measurement campaigns and electromagnetic simulations
- Integrate radar signatures into ray-tracing propagation simulation tools
- Train and apply artificial intelligence algorithms for object recognition, material property inference, and radar imaging
- Investigate physics-assisted AI methods to reduce computational complexity in simulations
- Incorporate radar backscattering data into a 3D Semantic Radio SLAM framework to enhance environmental understanding
Requirements
Degree required: Master's degree (MSc/M2) or engineering school degree
Degree offered: PhD
Language: English; no specific test score mandated
Instruction: English
- Strong academic background in signal processing, electromagnetics, radar, or telecommunications
- Interest in artificial intelligence and physics-based modeling
- Demonstrated ability to bridge theoretical models, numerical simulations, and experimental validation
- Strong motivation, scientific curiosity, and autonomy in research
- Open to students holding a Master's degree or equivalent engineering qualification
Funding & terms
How to apply
Deadline: 2026-11-30
Applicants must submit their complete application package through the official portal before the deadline. Ensure all required academic and personal documents are attached for consideration.
- CV
- Academic transcripts
- Motivation letter
Contact
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