Research Associate in Implementation of Signal Processing and Machine Learning Algorithms on FPGAs

Edinburgh - Kings Buildings, Midlothian, United Kingdom

The University of Edinburgh

Edinburgh. Extraordinary futures await. The University of Edinburgh is one of the world's top universities. Our entrepreneurial and cross-disciplinary culture attracts students and staff from across the globe, creating a unique Edinburgh...

View company page

Grade UE07: £39,347.00 - £46,974.00 per annum 

College of Science & Engineering / School of Engineering

Institute for Micro and Nano Systems (IMNS)

Full-time: 35 hours per week

Fixed-term: up to 18 months


This Research Associate position will contribute to the COG-MHEAR ( project within the Institute for Micro and Nano Systems at the University of Edinburgh, which is funded by  Engineering and Physical Science Research Council and in collaboration with colleagues at Universities of Edinburgh Napier, Glasgow, Nottingham, Wolverhampton, Manchester.

Your skills and attributes for success:

  • Strong background in FPGA based high performance circuit implementation. 
  • Strong background and experience in the implementation of audio and video based signal processing on FPGAs. 
  • Experience in the implementation of AI and machine learning algorithms. 
  • Independently driven and able to work with a multi-disciplinary team. 
  • Ability to meet strict deadlines. 

A full list of main responsibilities and the knowledge, skills and experience required for the role can be found in the full job description.

 click here for a copy of the full job description.

As a valued member of our team, you can expect:

  • A competitive salary.
  • An exciting, positive, creative, challenging and rewarding place to work. 
  • To be part of a diverse and vibrant international community.
  • Comprehensive Staff Benefits, such as a generous holiday entitlement, a defined benefits pension scheme, staff discounts, family-friendly initiatives, and flexible work options. Check out the full list on our staff benefits page (opens in a new tab) and use our reward calculator to discover the total value of your pay and benefits. 


Championing equality, diversity and inclusion

The University of Edinburgh holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. We are members of the Race Equality Charter and we are also Stonewall Scotland Diversity Champions, actively promoting LGBT equality. 


Prior to any employment commencing with the University you will be required to evidence your right to work in the UK. Further information is available on our right to work webpages (opens new browser tab).


The University is able to sponsor the employment of international workers in this role.  If successful, an international applicant requiring sponsorship to work in the UK will need to satisfy the UK Home Office’s English Language requirements and apply for and secure a Skilled Worker Visa.  

Key dates to note

The closing date for applications is 8th July 2024. 

Unless stated otherwise the closing time for applications is 11:59pm GMT. If you are applying outside the UK the closing time on our adverts automatically adjusts to your browsers local time zone. 

As a world-leading research-intensive University, we are here to address tomorrow’s greatest challenges. Between now and 2030 we will do that with a values-led approach to teaching, research and innovation, and through the strength of our relationships, both locally and globally.
Apply now Apply later
  • Share this job via
  • or

Tags: Athena Engineering FPGA Machine Learning Research Teaching

Perks/benefits: Career development Competitive pay Flex hours

Region: Europe
Country: United Kingdom
Job stats:  7  0  0

More jobs like this

Explore more AI, ML, Data Science career opportunities

Find even more open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general - ordered by popularity of job title or skills, toolset and products used - below.