Magnetic reservoir based energy-efficient computing

Aim

This project will establish a new paradigm of magneto-photonic neuromorphic devices, directly linking advances in fundamental spin dynamics, reservoir computing (RC) approaches and advanced materials to pressing technological challenges, such as the unsustainable rise in energy consumption from information and communication technologies (ICTs). 

Objectives

1. Demonstrate heterostructures formed by quantum magnets + plasmonic layer + substrates as nonlinear, memory-rich dynamical elements suitable for RC.
2. Develop and compare device architectures: (i) time-multiplexed single-node reservoirs, and (ii) spatially coupled magnetic arrays.
3. Integrate photonic and magnonic coupling for scalable, multimodal reservoirs.
4. Quantify computational performance and efficiency on benchmark tasks and against CMOS and photonic RC standards.
5. Establish design rules for future hybrid magneto-photonic neuromorphic processors.

Description

The exponential growth of ICTs is driving energy consumption towards unsustainable levels, motivating radical innovations in low-power information processing. Neuromorphic and RC offer promising routes to energy-efficient computation by exploiting the intrinsic dynamics of physical systems, rather than relying on conventional von Neumann architectures.

Novel device concepts that unite non-rare element magnets, photonic layers and CMOS-friendly substrates presents an untapped opportunity in this context. Its defining attributes—ultrafast optical control of spin states, strong nonlinearity from magnetization precession and spin-phonon interactions, and multi-modal (spin, phonon, photon) coupling—map directly onto the requirements of RC. In this context, RC leverages a high-dimensional, nonlinear dynamical system as a “reservoir” to transform inputs into separable states, requiring only a linear readout for training. Leveraging these properties with novel quantum materials enable the creation of magnetic reservoir processors that combine low-energy operation with ultrafast bandwidth and dense integration, addressing urgent demands for sustainable AI and edge computing.

This project will establish a new paradigm of magneto-photonic neuromorphic devices, directly linking advances in fundamental spin dynamics to critical technological challenges. The proposed research will:

• Advance fundamental knowledge of nonlinear spin dynamics under ultrafast optical control, with cross-disciplinary implications for condensed matter physics, spintronics, and photonics.
• Enable transformative technologies for neuromorphic and edge computing by combining ultralow energy operation with ultrafast temporal response.
• Contribute to sustainability by addressing the rapidly growing energy footprint of ICTs, aligning with global efforts towards net-zero digital infrastructure.
• Train a new generation of researchers at the intersection of magnetism, photonics, and neuromorphic computing.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.

Full funding is available for this position