This is a 4-year integrated PhD (iPhD) programme and is part of the UKRI AI Centre for Doctoral Training in AI for Sustainability (SustAI) under the theme Sustainable AI https://sustai.info/. Check this link for info on how to apply: Optoelectronic nanodevices for sustainable AI hardware at University of Southampton on FindAPhD.com

The aim of this project is to develop a new form of neuromorphic systems that merge photonic, electronic and ionic effects, bringing new prospects for in-memory computing and artificial visual memory applications. This will be achieved upon developing photoelectric memories fabricated with more sustainable processes and greener materials. First, you will develop single devices that emulate biological synapses in the human visual system, capable of detecting (in analogy to the retina in the eye) and memorising or even processing images (like the visual cortex in the brain). Then, you will design and implement a novel neuromorphic optoelectronic array that will perform certain neuromorphic functionalities, e.g., pattern recognition tasks. Finally, you will assess the sustainability of this approach by applying life-cycle assessment techniques to each step of the fabrication process.

 This novel electronic technology can effectively emulate synaptic weights and may be programmable both via light and voltage. This provides additional flexibility for implementing both synaptic weight updates as well as homeostatic effects. Furthermore, the technology relies on low-temperature processes and can thus be integrated on flexible substrates, which paves the way to incorporation of AI functionalities to wearable devices.

 The outcomes of your research can have various applications, such as Internet of Things (IoT) devices for visual data communication, human/environment detection/tracking, Augmented/Virtual Reality, etc. 

https://www.findaphd.com/phds/project/polyoxometalate-memories-for-low-power-ai-hardware/?p178320

This PhD project focuses on using polyoxometalates (POMs) to develop next-generation nanoscale devices for memory and neuromorphic computing. Leveraging POMs' unique electronic properties, you will design and optimise devices for high stability, energy efficiency, and scalability. Applications include AI hardware and energy-efficient, real-time processing for edge devices.

This PhD project offers a unique opportunity to work on groundbreaking nanoscale devices for memory and neuromorphic computing using polyoxometalates (POMs), a materials class with exceptional stability and tunable electronic properties. As silicon-based systems reach their limits, the need for alternative materials and architectures is critical. In this project, you will lead the way in developing POM-based devices to transform data storage and computing. Specifically, you will explore POMs as active materials for resistive switching devices, such as resistive RAM (ReRAM), and design POM-based devices that mimic synaptic plasticity, enabling brain-like, parallel computation. You will optimise the device fabrication and materials processing parameters that maximise the stability, energy efficiency, and speed of POM-based devices.

https://www.findaphd.com/phds/project/nanodevices-for-next-generation-memory-and-neuromorphic-computing-applications/?p178329

This PhD project involves developing nanoscale optoelectronic devices for next-generation memory and neuromorphic computing. You will explore advanced materials and nanopatterning techniques to create flexible, brain-inspired devices that emulate neural networks, enabling AI in wearables. Join the Flexible Nanoelectronics Lab to impact IoT, edge computing, and data storage.

This PhD project offers an exciting opportunity to work on the cutting-edge design and development of nanoscale devices, specifically tailored for next-generation memory systems and neuromorphic computing applications, namely the development of optoelectronic devices and systems that can realistically emulate biological neural networks.

In this project you will explore advanced materials and novel nanopatterning techniques to develop the next generation of nanodevices. High throughput fabrication of nanodevices on large areas will be pursued using organic and inorganic materials processed via solution-based or sputtering techniques, compatible with flexible substrates.

This novel electronic technology can effectively emulate synaptic weights and may be programmable both via light and voltage/current. Furthermore, the technology relies on low-temperature processes and can thus be integrated on flexible substrates, which paves the way to incorporation of AI functionalities to wearable devices.