Robin Msiska

Robin Msiska

Robin Msiska

Positions PhD Candidate
Email robin.msiska@uni-due.de
Phone 0203-379-4719
Office MG 385
Address

University of Duisburg-Essen
Faculty of Physics
TWIST Group
Lotharstraße 1
47057 Duisburg

Contact Researcher-ID: FQF-3718-2022

 

 

Fakultät für Physik, Theoretische Physik

Address
Lotharstraße 1
47057 Duisburg
Room
MG 385

Functions

  • Wissenschaftliche/r Mitarbeiter/in, Arbeitsgruppe Prof. Everschor-Sitte

Current lectures

No current lectures.

Past lectures (max. 10)

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.

    Journal articles

  • Msiska, Robin; Love, Jake; Mulkers, Jeroen; Leliaert, Jonathan; Everschor-Sitte, Karin
    Audio Classification with Skyrmion Reservoirs
    In: Advanced Intelligent Systems Vol. 5 (2023) Nr. 6, 2200388
  • Lee, Oscar; Msiska, Robin; Brems, Maarten A.; Kläui, Mathias; Kurebayashi, Hidekazu; Everschor-Sitte, Karin
    Perspective on unconventional computing using magnetic skyrmions
    In: Applied Physics Letters (APL) Vol. 122 (2023) Nr. 26, 260501
  • Love, Jake; Msiska, Robin; Mulkers, Jeroen; Bourianoff, George; Leliaert, Jonathan; Everschor-Sitte, Karin
    Spatial analysis of physical reservoir computers
    In: Physical Review Applied Vol. 20 (2023) Nr. 4, 044057
  • Msiska, Robin; Rodrigues, Davi R.; Leliaert, Jonathan; Everschor-Sitte, Karin
    Nonzero Skyrmion Hall Effect in Topologically Trivial Structures
    In: Physical Review Applied Vol. 17 (2022) Nr. 6, 064015

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.