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Research Fellow in Energy-Efficient Machine Learning for Mobile/Embedded Systems

Cyber Physical Systems

Location:  Highfield Campus
Salary:   £30,942 to £38,017
Full Time Fixed Term (until 31/10/2022)
Closing Date:  Friday 22 November 2019
Reference:  1193719FP

We are excited to offer a new global opportunity to work with international researchers at the forefront of novel machine learning hardware. The University of Southampton has teamed up with Imperial College, the University of Toronto, and the University of California Los Angeles to launch a new Centre for Spatial Computational Learning ( The Centre is funded by the UK’s Engineering and Physical Sciences Research Council, and works in partnership with key industrial partners (Arm, Corerain Technologies, Imagination Technologies, Maxeler Technologies, Xilinx). Our aim is to bridge hardware, software and theory, to develop new highly-efficient algorithms and architectures that naturally compute over space as well as time.

You will have the opportunity to collaborate across the remit of the Centre and will aim to spend some time each year working at one of the international partner institutions developing and deepening our collaborations. We expect that such exchanges will occur annually, and be of a duration between a week and a month. Appointees will have considerable freedom to help define medium-term research areas within the remit of the Centre.

Applicants should be good communicators, take a methodological approach to their work, be hard-working and reliable, and have the ability and desire to travel and collaborate internationally. They should have a PhD* or equivalent experience in electronic engineering, computer science, or a related discipline, and a strong track record in at least one of the following areas:

  • Hardware design for machine learning/deep learning
  • Software/algorithm development for machine learning/deep learning
  • Hardware/software co-design for improved performance/efficiency
  • Efficient software development for mobile/embedded systems
  • Run-time software adaptation for system resource management

*Applications will be considered from candidates who are working towards or nearing completion of a relevant PhD qualification. The title of Research Fellow will be applied upon completion of PhD. Prior to the qualification being awarded the title of Senior Research Assistant will be given.

If you wish to have an informal discussion about the post prior to applying, please email Professor Geoff Merrett (

The University

The post will be held in the School of Electronics and Computer Science (ECS) at the University of Southampton. ECS hosts a strategic research centre in machine intelligence (, an EPSRC Centre for Doctoral Training in Machine Intelligence for Nano- Electronic Devices and Systems (spanning hardware and software), and the Arm-ECS Research Centre ( an award-winning industry-academia collaboration between the university and Arm Research.

Ranked in the top 1% of universities globally and among the UK's top 20 for research, the University of Southampton has an international reputation for its research, teaching and enterprise activities. We have a range of staff development programmes and a unique mentoring and wellbeing scheme. There is a great range of benefits that includes a contributory pension scheme; generous holiday entitlement; subsidised health and fitness facilities; cycle to work scheme; a range of discounts.

Application Procedure:

You should submit your completed online application form at The application deadline will be midnight on the closing date stated above. If you need any assistance, please call Sam Stubbs (Recruitment Team) on +44 (0)23 8059 4046. Please quote reference 1193719FP on all correspondence.

The closing date for this job opportunity has now passed, and applications are no longer being accepted for this position

Further details:

We aim to be an equal opportunities employer and welcome applications from all sections of the community. Please note that applications from agencies will not be accepted unless indicated in the job advert.

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