Ph.D. Thesis (M/F): Fabrication, Characterization and Frequency-Domain Learning in Spintronic RF Neural Networks

Job Description

Thesis Subject

This Ph.D. project aims to develop spintronic radio-frequency nanodevices as building blocks for hardware neural networks operating and learning in the frequency domain. The research will focus on the fabrication, electrical and RF characterization, and algorithmic exploration of spintronic nanodevices whose nonlinear dynamics, frequency response, and device-to-device variability can be used for neuromorphic computing.
The candidate will contribute to the nanofabrication of spintronic devices, their experimental characterization under RF excitation, and the development of dedicated learning algorithms adapted to spintronic RF neural networks. A central objective will be to encode, process, and train information directly in the frequency space, taking advantage of the physical properties of the devices. The project will combine experimental spintronics, RF measurements, and machine-learning approaches to demonstrate learning capabilities in hardware-compatible spintronic systems.

Activities

• Nanofabrication of spintronic RF nanodevices using cleanroom processes
• Optimization of device geometry and materials for frequency-domain neuromorphic operation
• Electrical and RF characterization of spintronic nanodevices
• Measurement of nonlinear, resonant, and frequency-dependent responses under RF excitation
• Design of experimental protocols for frequency-domain encoding, processing, and learning
• Development of learning algorithms adapted to RF spintronic neural networks
• Implementation and validation of hardware-compatible training strategies
• Analysis of the impact of device variability, noise, and imperfections on learning performance
• Collaboration with a multidisciplinary team combining spintronics, nanofabrication, RF measurements, and neuromorphic computing

Required expertise

Strong background in experimental physics, nanophysics, or spintronics
• Experience or strong interest in nanofabrication and cleanroom processes
• Experience in electrical and/or RF measurements of nanodevices
• Expertise in Python and/or machine-learning algorithms
• Interest in hardware neural networks, neuromorphic computing, and physics-based learning
• Ability to work at the interface between experiments, device physics, and algorithms

Your Work Environment

The work will be carried out at the Albert Fert Laboratory, in the "Neuromorphic Physics" team exploring the use of nanodevices and their multiple functionalities for bio-inspired computing. The team includes two permanent CNRS researchers, two Thales researchers, 4 post-docs, and 4 PhD students.

Constraints and risks

N/A

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

The research professions