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  Silicon Synapses for Artificial Intelligence Hardware


   Faculty of Engineering and Physical Sciences

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  Dr Firman Simanjuntak  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

Supervisory Team:  Firman Simanjuntak

Project description

Sustainable Electronics Technologies (SET) Research Group, University of Southampton

PhD studentship in Nanoelectronics

The University of Southampton is ranked 1st in electrical and electronic engineering subjects (The Guardian University Guide, UK universities 2023 rankings). SET, one of the leading research groups in the UK, envisions offering unique solutions to the real-world problem by delivering efficient electronics while addressing all aspects of sustainability. Our research interests include neuromorphic computing, heterogeneous integration, and 2D material-based and wearable electronics. We have diverse expertise ranging from devices to systems, and our state-of-art research facilities and strong collaborations with industry enable us to explore new ways to innovate advanced nanoelectronics. We are seeking exceptional candidates to join our team, interested in devoting their passion to addressing some of the challenges we have identified. More information on the SET research group can be accessed at: https://www.set.ecs.soton.ac.uk/

Project description: Electronic devices, such as computers, mobile phones and data centres, account for a significant amount of the world's energy consumption; we need to invent novel technologies to deliver high-speed, low-power and efficient computing and communication. This PhD studentship aims at developing neuromorphic chips for artificial intelligence (AI) hardware accelerators. You will exploit the architecture of memristive technologies to deliver faster, smaller, and lower-powered AI chips than the conventional chips that your gadgets have. Memristive nanodevices are an emerging nanoelectronic technology with unique capabilities in mimicking the communication of neurons.You can create a neural network by interconnecting a massive number of these devices. We will train you how to design and fabricate a massive array of memristive prototypes, investigate their reliability, and integrate them to build a silicon neural network. The final objective of this project is to build an application demonstrator employing AI chips to run neuromorphic computations on an FPGA. You will have access to our state-of-art cleanroom and materials & electrical characterisation facilities, and we have excellent research staff and technicians that will support your research (https://www.southampton-nanofab.com/). We will encourage you to attend international conferences in the UK and abroad to present your research work and guide you to publish your results in high-impact journals.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent) or an MEng/MSc (or equivalent, or near completion) with first-class honours or distinction in Physics, Engineering, Materials Science or a closely related subject, such as Electronics and Chemistry. Basic knowledge of nanotechnologies or electronics is a plus.

Closing date: 31 August 2023.

Funding: For UK students, Tuition Fees and a stipend of £17,668 tax-free per annum for up to 3.5 years.

How To Apply

Applications should be made online. Select programme type (Research), 2022/23, Faculty of Physical Sciences and Engineering, next page select "PhD Nanoelectronics (Full time)". In Section 2 of the application form, you should insert the name of the supervisor (Firman Simanjuntak)

Applications should include:

Research Proposal (free form, 1 page A4)

Curriculum Vitae

Two reference letters

Degree Transcripts to date

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk)

Overseas applicants should achieve an IELTS score of 6.5 with at least 6.0 in each competency. Click here for information on this and other accepted English language tests.

For further information please contact: [Email Address Removed]


Chemistry (6) Computer Science (8) Engineering (12) Physics (29)

 About the Project