Ph.D. Thesis (M/F): Fabrication, Characterization and Frequency-Domain Learning in Spintronic RF Neural Networks
Core
Develop spintronic radio-frequency nanodevices as building blocks for hardware neural networks operating and learning in the frequency domain.
Role type
Ph.D. researcher in experimental spintronics and neuromorphic physics
Builds
Hardware-compatible spintronic RF neural networks
Domain
Physics / Nanotechnology / Neuromorphic Computing
Deliverable
production ML models | physical/clinical work
Required skills
nanofabrication, cleanroom processes, electrical measurements, RF measurements, Python, machine learning algorithms
Preferred skills
experimental physics, nanophysics, spintronics, hardware neural networks
Technologies
Python
Responsibilities
Nanofabrication of spintronic RF nanodevices, electrical and RF characterization of nanodevices, design of experimental protocols for frequency-domain learning, development of learning algorithms for RF spintronic neural networks, analysis of device variability impact on learning performance
Seniority
Ph.D. candidate