|Location:||Newcastle upon Tyne|
|Salary:||Research Associate: £33,966 to £44,263 per annum Senior Research Associate: £45,585 per annum|
|Placed On:||18th September 2023|
|Closes:||20th October 2023|
We are a world class research-intensive university. We deliver teaching and learning of the highest quality. We play a leading role in economic, social and cultural development of the North East of England. Attracting and retaining high-calibre people is fundamental to our continued success.
The Microsystems Research Group is looking for a motivated Research Associate/Senior Research Associate to support the School of Engineering's EPSRC project "UKRI-RCN: Exploiting the dynamics of self-timed machine learning hardware (ESTEEM)".
The Microsystems Group is prominent in the UK. It has an international reputation for world-leading research across microelectronics design and computer systems engineering with ten academic members, 10+ Research Assistants/Associates, Senior Research Associates, and 30+ PhD students.
This project will investigate opportunities for improving performance and energy efficiency in artificial intelligence hardware created by the inherent time and power elasticity of self-timed circuits. The project will lay foundation to a new design methodology for building electronic devices and systems with machine learning (ML) capabilities at the micro- and nano-scale granularity. Those devices will be widely leveraged in many at-the-edge applications such as environmental sensors, traffic monitors, wearables, as well potential commodity ML-enhanced devices that can be used as building blocks in computer systems of the future.
The project outcomes in theory and design methodology will be validated by means of extensive simulations, prototyping, IC fabrication and testing, and, ultimately, via an embodiment of the new hardware solutions into a concrete Internet of Things (IoT) application. A particularly challenging and breaking through validation will be the development and fabrication of the first asynchronous machine learning integrated circuit using flexible substrates.
Within the project, the nominated candidate will work on both aspects, the machine learning hardware, both training and inference, and self-timed IC design and test.
The nominated candidate will perform tasks under the guidance of the Principal Investigator and three Co-Investigators, work closely with a multidisciplinary team, external academic partners, and industrial partners (PragmatIC, Cambridge Future Tech).
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We are committed to building and maintaining a fair and inclusive working environment and we would be happy to discuss arrangements for flexible and/or blended working. Additionally, the post is eligible for blended working between campus and home.
To apply, please complete an online application and upload a CV and cover letter. Your cover letter is a supporting statement, and you should outline how you meet the essential criteria of the role and evidence this with examples.
For informal enquires please contact Professor Alex Yakovlev email@example.com
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