PhD in AI for Listening Fatigue from Wearables at the University of Southampton
PhD opportunity at the University of Southampton
in
AI, machine learning, biomedical engineering, neuroscience, psychology, and acoustics
.
The project,
The Attentive Ear: AI to Decode Listening Fatigue from Wearables
, is based in the Faculty of Engineering and Physical Sciences and the Institute of Sound and Vibration Research (ISVR). It focuses on using
multimodal deep learning
and
transformer architectures
to combine asynchronous wearable and physiological signals such as
EEG, pupillometry, ECG, galvanic skin response, and fNIRS
. The aim is to distinguish genuine cognitive listening fatigue from general acoustic arousal in noisy environments and support next-generation smart hearables and assistive listening devices.
You will work with calibrated listening booths, acoustic laboratories, and multi-channel biosignal datasets collected in speech-in-noise paradigms. The technical stack includes
PyTorch
, self-supervised learning, sequence modelling, and high-performance computing on the University’s
Iridis
cluster. The project combines experimental hearing science with modern AI methods and offers training in data management, scientific writing, intellectual property, and conference presentation.
Eligibility:
a UK 2:1 honours degree or equivalent in computer science, AI, electrical/electronic engineering, data science, biomedical engineering, or a related field. Strong Python and deep learning experience are essential. Experience with biosignals, transformers, acoustics, Linux, or Git is desirable.
Funding:
competition-funded PhD project for students worldwide. Funding options mentioned include Horizon Europe fee waivers, Presidential Bursaries, and school studentships that may cover UK tuition fees plus a stipend. Funding is awarded on a rolling basis, so early application is encouraged.
How to apply:
submit an online application for Research entry in 2027/28, Faculty of Engineering and Physical Sciences. Search for
PhD Engineering & the Environment (7175)
, choose full-time or part-time, and add the supervisor name in section 2. Include your CV, two academic references, transcripts/certificates, and English language evidence if applicable.
Deadline:
2027-01-31.
