Harnessing topological nano-whirls to enable brain-like computing - Royal Society PhD Scholarships (Magnetism)

Title: Harnessing topological nano-whirls to enable brain-like computing Motivation: Today's computing ecosystem uses 10% of global electricity and contributes 2% of emissions (at par with aviation!). With the rise of AI, autonomous devices and big data, this energy demand will increase sharply, putting strain on global resources. Heart of the problem is that charge-based silicon platforms are volatile, inefficient and serial. This has resulted in a quest for novel quantum materials hosting rich emergent physical properties that can be harnessed to yield high-speed and energy-efficient functionalities. Topologically-rich antiferromagnets (AFMs), consisting of vortex-like whirling spin textures, are especially promising in this regard, as they host a robust non-volatile magnetic order. It has been theoretically predicted that such nano-whirls can move at incredible speeds of kilometres/second and host rich dynamics up to terahertz frequencies, i.e. 100-1000 times faster than conventional silicon devices. Such magnetic ‘nano-whirls’ could be used to power reservoir computing or logic-in-memory, enabling next-gen AI hardware. We have pioneered the discovery and control of AFM solitons at room temperature, thus, experimentally opening the field of topological antiferromagnetism. These breakthroughs have been published in Nature (2021), Nature Materials (2024a), Nature Materials (2024b), Nature Communications (2021) etc. Project Scope: The goal of these DPhil projects is to push this young field toward the ultra-fast dynamical frontiers and thus open doors to its practical utilisation. Students will learn to use cutting-edge spectro-microscopy and dynamical imaging techniques to explore fast AFM dynamics. They will pioneer a versatile toolbox – involving quantum materials design, spintronic control, combined with electrical and optical excitation – to enable targeted spatio-temporal control of AFM nano-whirls. These fundamental breakthroughs are urgently needed to enable transformative next-generation applications, like terahertz nano-oscillators, AFM magnon nano-emitters and terahertz reservoir computing to develop next-gen AI. We are looking for passionate physics students with: • a strong academic track record and good exposure to hands-on research • experience in coding or simulations (e.g. using Python, MATLAB etc.) • a keen interest in both experimental research and computational modelling If you want to join our team, contact Hariom Jani (hariom.jani@physics.ox.ac.uk) with your CV and a statement of interest. See attached document and URL for further details.