Robin Msiska
Dr. Robin Msiska
| Positions | Postdoctoral Researcher |
| robin.msiska@uni-due.de | |
| Phone | 0203-379-4719 |
| Office | MG 385 |
| Address |
University of Duisburg-Essen |
| Contact | Researcher-ID: FQF-3718-2022 |
Fakultät für Physik, Theoretische Physik
47057 Duisburg
Functions
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Wissenschaftliche/r Mitarbeiter/in, Arbeitsgruppe Prof. Everschor-Sitte
Current lectures
No current lectures.
Past lectures (max. 10)
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WiSe 2021
The following publications are listed in the online university bibliography of the University of Duisburg-Essen. Further information may also be found on the person's personal web pages.
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Rechnen mit magnetischen WirbelnIn: Physik in unserer Zeit, Vol. 55, 2024, Nr. 4, pp. 183 – 189DOI (Open Access)
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Audio Classification with Skyrmion ReservoirsIn: Advanced Intelligent Systems, Vol. 5, 2023, Nr. 6, 2200388DOI (Open Access)
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Perspective on unconventional computing using magnetic skyrmionsIn: Applied Physics Letters (APL), Vol. 122, 2023, Nr. 26, 260501DOI, Online Full Text (Open Access)
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Spatial analysis of physical reservoir computersIn: Physical Review Applied, Vol. 20, 2023, Nr. 4, 044057DOI, Online Full Text (Open Access)
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Nonzero Skyrmion Hall Effect in Topologically Trivial StructuresIn: Physical Review Applied, Vol. 17, 2022, Nr. 6, 064015DOI (Open Access)
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Skyrmion Hall Effect in Topologically Neutral Structures and Performance Studies of Skyrmion-Based Reservoir ComputingDuisburg, Essen, 2025DOI, Online Full Text (Open Access)
Journal articles
Thesis
The manipulation of magnetic textures at the nanoscale presents new horizons for computing technology. As conventional electronic computers reach their physical limits, magnetic skyrmions offer a promising solution for next-generation information processing, requiring minimal energy and electric current for manipulation.
My research focuses on current-driven dynamics of magnetic skyrmions and their potential for unconventional computing, utilising both analytical and numerical modelling techniques —primarily within the micromagnetic framework.
I specialise in physical reservoir computing, developing frameworks to evaluate the general computational capacities of physical substrates. Taking magnetic skyrmion reservior systems as a prototype platform, we have demonstrated how in-materia computing enables direct spatio-temporal pattern recognition without the need for millions of interconnected artificial neurons. Our implementation achieved state-of-the-art performance in classification tasks such as spoken digit recognition.