RESEARCH FELLOW - MULTIMODAL MACHINE LEARNING AND EDGE AI

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Merchiston
24/07/2025
Research Fellow - Multimodal Machine Learning and Edge AI
The job requirements are detailed below. Where applicable the skills, qualifications and memberships required for this job have also been included.
Job details

JOB REFERENCE

0000022080
Job description
COG-MHEAR is a world-leading cross-disciplinary research programme funded under the EPSRC Transformative Healthcare Technologies 2050 Call. The programme aims to develop truly personalized multimodal hearing assistive technology. It includes academic partners from 6 other UK Universities and a strong User Group comprising industrial and clinical collaborators, and end-user engagement organisations. For more details, visit our website: http://cogmhear.org.uk.
Currently, Edinburgh Napier University is seeking to appoint a full-time Research Fellow with expertise in Multimodal Hearing Assistive Technology and Edge AI Solutions to take a leading role in COG-MHEAR under the direction of Professor Amir Hussain, developing Edge based audio-visual speech enhancement and separation technologies in real-life environments.

WHAT WE WILL NEED FROM YOU:

• PhD degree in a relevant subject or working towards (Viva stage)
• Strong background in deep neural networks and multimodal hearing-aid signal processing,
• Experience in real-time implementation of federated AI and Edge-based machine learning applications
• Strong publication record in top journals and conferences
• Proven ability to solve real-world problems independently and make original contributions to collaborative research
• Excellent programming, quantitative research and teamworking skills
  For a full job description and comprehensive list of duties,

As Research Fellow on the COG-MHEAR project, you will have the opportunity to use your strong background in deep neural networks and multimodal hearing-aid signal processing to undertake world-leading research in the design, integration and Edge-implementation/testing of multimodal machine learning models.
Your experience in real-time implementation of federated AI and Edge-based machine learning applications will serve you well as you liaise with project researchers, collaborating companies, clinicians and end-users in the User Group, to ensure overall COG-MHEAR programme goals are met and allow you to contribute to the collaborative design, integration and optimisation of real-time software and hardware prototypes.
We anticipate that you will also have a strong publication record in top journals and conferences, allowing you to effectively prepare peer-reviewed publications for high-impact journals and conferences while managing, supervising, and administering all research duties associated with the post.
In return, you will have the opportunity to contribute your knowledge and expertise to something that could make a real-world difference, secure future research funding and work alongside renowned academics and researchers in ENU’s School of Computing, Engineering and the Built Environment.
If you are someone with expertise in multimodal speech processing and AI algorithms to run directly on endpoint devices, with excellent programming, quantitative research and teamworking skills, then we would love to hear from you.