- • Bio-inspired computing architectures based on spintronic and hybrid systems
- • Magneto-ionic control of magnetic properties and adaptive spintronic devices
- • Spin-torque nano-oscillators for unconventional and neuromorphic computing
- • Magnetic tunnel junctions for stochastic and probabilistic computing
- • Thermal activation and noise-driven dynamics in spintronic devices
- • Spintronic oscillator arrays and synchronization phenomena
- • Magnonics and frequency-based information processing
- • Skyrmion-based devices for neuromorphic and unconventional computing
- • Dynamical spintronic networks and reservoir computing
- • Machine-learning-assisted training of spintronic systems
- • Data-driven analytical and micromagnetic modeling approaches
- • Hybrid spintronic circuits and emerging computing architectures
- • Large-scale simulation of spintronic-based computing systems


Prof. Flavio Abreu Araujo began his scientific career in 2010 in spintronics and nanomagnetism, working on the fabrication, characterization, and modeling of spin-torque vortex oscillators. In 2015, he pioneered research at the interface of spintronics and artificial intelligence by demonstrating the first spintronics-based nano-neuron, helping establish the field of Neuromorphic Spintronics. His work focuses on developing nanoscale spintronic computing devices that drastically reduce energy consumption while enhancing computational capability, drawing inspiration from the efficiency of biological neural systems.
Liza Herrera Diez is a CNRS research director at the Centre for Nanoscience and Nanotechnology in Palaiseau, France. She studied physical chemistry at the National University of Córdoba in Argentina and conducted her PhD work at the Max Planck Institute for Solid State Research in Germany while enrolled in the physics doctoral school at Ecole Polytechnique Fédérale de Lausanne.
She has an interdisciplinary background in physics and chemistry. Her research focuses on magneto-ionics, which combines the analogue functionality of ionics with the binary nature of magnetism to develop reconfigurable multistate spintronic nanodevices. She coordinated the MSCA Innovative Training Network MagnEFi on electric-field effects in magnetic materials and devices, and currently coordinates the EU Pathfinder project METASPIN, which explores magneto-ionic approaches to design multifunctional nanodevices for neuromorphic hardware.
Mathias Kläui is professor of physics at Johannes Gutenberg-University Mainz and adjunct professor at the Norwegian University of Science and Technology.
He received his PhD at the University of Cambridge, after which he joined the IBM Research Labs in Zürich. He was a junior group leader at the University of Konstanz and then became associate professor in a joint appointment between the EPFL and the PSI in Switzerland before moving to Mainz. His research focuses on nanomagnetism and spin dynamics on the nanoscale in new materials. His research covers from blue sky fundamental science to applied projects with major industrial partners. He has published more than 400 articles and given more than 250 invited talks. He is a Fellow of the IEEE, IOP and APS, the European Academy of Sciences and the German National Academy of Science and Engineering and has been awarded a number of prizes and scholarships. He has been one of the 2020/2021 IEEE Magnetics Society Distinguished Lecturers.
Contact details and more information at www.klaeui-lab.de
Damien Querlioz
Philippe Talatchian is a research scientist at CEA Grenoble, working at SPINTEC on spintronic devices and architectures for unconventional and neuromorphic computing. He obtained his PhD from Université Paris-Saclay under the supervision of Julie Grollier, followed by a postdoctoral position at the University of Maryland and NIST in the group of Mark Stiles. His research focuses on exploiting the intrinsic dynamics of nanoscale magnetic devices, including stochastic magnetic tunnel junctions and spin-torque nano-oscillators, for probabilistic computing, hardware neural networks, and in-situ learning. At SPINTEC, he contributes to the development of energy-efficient spintronic approaches for brain-inspired computing, from device physics to system-level demonstrations and neuromorphic algorithms.