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Research Associate in Neuromorphic Computing

The University of Manchester - Department of Computer Science

Location: Manchester
Salary: £36,024 to £44,263 per annum, depending on relevant experience. Grade 6
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 17th May 2024
Closes: 7th June 2024
Job Ref: SAE-025561

Job reference: SAE-025561

Salary: Grade 6 £36,024 to £44,263 per annum, depending on relevant experience

Faculty/Organisational Unit: Science and Engineering

Location: Oxford Road

Employment type: Fixed Term

Division/Team: Department of Computer Science

Hours Per Week: 1 FTE

Closing date (DD/MM/YYYY): 07/06/2024

Contract Duration: 18 months

School/Directorate: School of Engineering

Applications are invited to join the University of Manchester as part of the Horizon Europe research project NimbleAI. The project brings together 19 partners from across Europe, to research and develop next-generation integrated sensing-processing neuromorphic devices. The consortium will leverage key principles of energy-efficient visual sensing and processing in biological eyes, and harness the latest advances in 3D stacked silicon integration, to create an integral sensing-processing neuromorphic architecture that efficiently and accurately runs computer vision algorithms in resource- and area-constrained endpoint chips.

Manchester has a long and distinguished track record in the research and teaching of core Computer Science, and across interfaces to adjacent disciplines. Founded upon the pioneering work of Williams, Kilburn and Turing, the Department was the first academic Department of Computer Science in the UK and one of the first to run an undergraduate programme. The research strength of the school is reflected in consistently strong returns in UK research assessment exercises (5* in RAE 2000, 2nd in Research Power in RAE 2008, and ranked equal 1st for research environment in REF2014 and REF2021).

A key responsibility of the University of Manchester within the Nimble AI project is the development of a spiking neural network engine, which will form part of the early sensing and perception pipeline within the integrated system. This will act as a pre-processor for the event-based visual stream, facilitating operations such as: foveation through identification of regions of interest, initial classification of moving objects, removal of noise and unimportant features, and configuration/selection of downstream processing engines and/or resource requirements such as DVFS.

The successful candidate will be responsible for the development and mapping to hardware of SNN-based vision algorithms for the above tasks, and the development of tools and techniques to tailor system performance to specific applications (e.g. autonomous driving).

The Department of Computer Science is strongly committed to promoting equality and diversity, including the Athena SWAN Charter for gender equality in higher education. The School holds a Bronze Award which recognises their good practice in relation to gender; including flexible working arrangements, family-friendly policies, and support to allow staff to achieve a good work-life balance.

We particularly welcome applications from women and other under-represented groups for this post. Appointment will always be made on merit. For further information, please visit https://www.cs.manchester.ac.uk/connect/social-responsibility/responsible-processes/.

The University will actively foster a culture of inclusion and diversity and will seek to achieve true equality of opportunity for all members of its community.

Enquiries about the vacancy, shortlisting and interviews:
Name: Dr Oliver Rhodes
Email: oliver.rhodes@manchester.ac.uk

General enquiries:
Email: People.recruitment@manchester.ac.uk

Technical support:
https://jobseekersupport.jobtrain.co.uk/support/home

This vacancy will close for applications at midnight on the closing date.

Please see the link below for the Further Particulars document which contains the person specification criteria.

SAE-025561 Research Associate in Neuromorphic Computing FP's

 

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