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Stefan Bleeck

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PhD in AI for Listening Fatigue from Wearables at the University of Southampton University of Southampton in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Competition-funded PhD project for students worldwide. Funding opportunities include Horizon Europe fee waivers covering the difference between overseas and UK fees for qualifying students, Presidential Bursaries covering the difference between overseas and UK fees for top-ranked applicants, and school studentships that typically cover UK-level tuition fees plus a living-cost stipend for top-ranked applicants. Funding is awarded on a rolling basis.

Deadline

Jan 31, 2027

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Country

United Kingdom

University

University of Southampton

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Keywords

Computer Science
Data Science
Psychology
Biomedical Engineering
Artificial Intelligence
Human-computer Interaction
Neuropsychology
Acoustic
ML
bio engineering

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About this position

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.

Funding details

Competition-funded PhD project for students worldwide. Funding opportunities include Horizon Europe fee waivers covering the difference between overseas and UK fees for qualifying students, Presidential Bursaries covering the difference between overseas and UK fees for top-ranked applicants, and school studentships that typically cover UK-level tuition fees plus a living-cost stipend for top-ranked applicants. Funding is awarded on a rolling basis.

What's required

Applicants need a UK 2:1 honours degree or international equivalent in computer science, Artificial Intelligence, electrical and electronic engineering, Data Science, biomedical engineering, or a relevant discipline. Essential requirements include strong Python programming skills, hands-on experience with modern deep learning frameworks, primarily PyTorch, excellent written and verbal English communication, and the ability to manage interdisciplinary research tasks independently. Desirable experience includes processing physiological time-series or biosignals such as pupillometry/eye-tracking, ECG/HRV, and GSR/EDA, familiarity with transformer architectures, attention mechanisms, or self-supervised representation learning, basic acoustics or audio signal processing knowledge, and Linux/Git experience. Further training will be provided.

How to apply

Apply online for a Research programme for 2027/28 in the Faculty of Engineering and Physical Sciences. Select full-time or part-time, search for PhD Engineering & the Environment (7175), and add the supervisor name in section 2. Include your CV, two academic references, transcripts/certificates, and English language qualification if applicable.

More information can be found here

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