Advanced Information Storage


Digital information can be stored in different types of devices depending on the use and how frequently the data need to be accessed. In a typical computer, data that are infrequently accessed are stored in hard disk drives (HDDs). These can be magnetic devices with high storage density in which binary numbers (“0” and “1”) are encoded in the polarity (spin “up” and “down”) of a magnetic medium. Magnetic data storage is cheap and non-volatile, meaning the data persists after power to the device is cut off, but the speed of accessing the data is relatively slow because the read/write procedures involve moving mechanical parts. Data being frequently required, on the other hand, needs to be accessed on a much faster timescale. Memory devices dedicated to this purpose are volatile random-access memories (RAMs) — solid-state electronic devices in which information is electrically stored. The slow non-volatile and fast volatile memories are physically separated in computers (known as von Neumann architecture), resulting in significant latency as the fast processors must wait for the slow data fetching. This has become the key performance bottleneck for the artificial intelligence (AI) related workloads.

This PhD aims to investigate strategies for designing and producing a universal memory device in micro/nano scale that combines the best of both worlds: low-cost, non-volatile, high-density as a HDD, and robust, fast access as a RAM. This represents a collocation of memory and processing units, and underpins a number of emerging technologies such as MRAM and neuromorphic computing.

The PhD will be trained to carried out research using the national synchrotron radiation and neutron scattering facilities at Harwell Campus and the Materials Innovation Factory at Liverpool. We also benefit from broad national and international collaborations which the PhD is expected to closely involve.


Applicants must hold, or be predicted to achieve, a UK first-class degree or equivalent in one of the following subjects: Physics, Material Science, Chemistry and Electronic Engineering. Laboratory research experience in physical subjects and relevant track records would be advantageous. Some programming experience using Python is desired. 

How to apply

Applicants are encouraged to send their (i) CV, (ii) abstract and outline of thesis of the last degree, and (iii) up to three representative publications to professor Wenqing Liu, on  for an informal discussion before submitting an online application.


Open to students worldwide

Funding information

Funded studentship

The studentship includes (i) full UK home tuition fees, (ii) a stipend of approximately £18,621.96  (2023/24) per annum (approximately £65,173.50 for 3.5 years) and (iii) a Research Training Support Grant of £5,000 in year one to use across the duration of the studentship.

Overseas applicants are welcome to apply but would be required to have other sources to pay the difference between the home and oversea tuition fees.




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