使用可穿戴设备AI解码听觉疲劳博士研究员
The attentive ear: AI to decode listening fatigue from wearables
原帖优先：申请材料、截止时间与资格以原帖和学校官方说明为准。
博士招生与资助公告
- 申请年度
- 2027；原帖说明：How to apply Apply now You need to: • choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences • select Full time or Part time • search for programme PhD Engineering & the Environment (7175) • add name of the supervisor in section 2 of the application Applications should include: • your CV (resumé) • 2 academic references • degree transcripts and certificates to date • English language qualification (if applicable)
- 最近申请截止
- 2027-01-31
- 全部申请截止
- （Europe/London）
- 资助原文
- Fees and funding We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students. Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants. Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants. Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered. For more information, please visit our postgraduate research funding pages.
- 资助条件
- 原帖为竞争性或附条件资助说明，未确认本项目获资助及申请人能获奖；不作为保证全奖。
- 原帖材料说明
- • your CV (resumé)
- • 2 academic references
- • degree transcripts and certificates to date
- • English language qualification (if applicable)
确定性信息来自对应版本的完整原帖。原帖只提供日期，日末和学校当地时区为本站转换假设。筛选使用最后申请截止；资助与录取以学校审核结果为准。
AI 中文速览
- 研究内容
- 本项目利用多模态深度学习Transformer模型，结合异步可穿戴信号（如脑电图、瞳孔测量法、心率、皮肤电导等），开发能够将真实的认知听觉疲劳与日常声学唤醒区分开来的AI引擎，为下一代智能助听设备提供支持。
- 申请条件
- 申请人必须持有英国2:1荣誉学士学位或国际同等学历（计算机科学、人工智能、电气与电子工程、数据科学、生物医学工程或相关学科）。必备技能包括精通Python编程、具备使用现代深度学习框架（主要是PyTorch）的实践经验、出色的英语书面和口头沟通能力，以及独立管理跨学科研究任务的能力。
- 待遇
- 学校为表现优异的申请者提供竞争性奖学金或校长奖学金，通常涵盖英国本土学费及生活费津贴，同时提供跨学科培训、高算力集群支持以及丰富的学术与行业交流机会。
- 申请方式
- 申请人需通过南安普顿大学系统在线申请，选择研究型项目（2027/28学年，工程与物理科学学院），搜索PhD Engineering & the Environment (7175)项目，并在申请的第二部分添加导师姓名。
- 材料清单
- 个人简历 (CV)
- 2份学术推荐信
- 至今为止的学位成绩单和证书
- 英语语言资格证明（如适用）
由 gemini-2.5-flash-lite 生成，博士岗判定置信度 100%。
岗位信息
- 最终轮次截止
- 2027-01-31
- 学科
- 计算机科学
- 合同类型
- 项目资助
- 本站收录
- 内容更新
- 导师
- Stefan Bleeck
- 来源
- 南安普顿大学博士研究项目招生 · 最近核对 2026-10-10
- 详情核验
判定依据（原文摘录）
- is_phd
Type of degree Doctor of Philosophy
- bachelor_ok
You must have a UK 2:1 honours degree or its international equivalent
原文
View all current projects
Postgraduate research project
The attentive ear: AI to decode listening fatigue from wearables
Funding
Competition funded
Competition funded
View fees and funding
Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree
2:1 honours degree
View full entry requirements
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date
31 Jan 2027
On this page
About the project
Potential supervisors
Entry requirements
Fees and funding
How to apply
Contact us
About the project
Potential supervisors
Entry requirements
Fees and funding
How to apply
Contact us
About the project
Listening in noisy environments is mentally exhausting, but human physiological responses move at fundamentally different speeds. This project uses multimodal deep learning transformers to combine asynchronous wearable signals (EEG, pupillometry, heart rate, skin conductance and others), creating an AI engine that isolates genuine cognitive listening fatigue from everyday acoustic arousal to power next-generation smart hearables.
The Challenge
Understanding speech in noisy environments demands heavy cognitive effort. However, identifying when a listener crosses from effortful listening into mental exhaustion remains an unsolved challenge. Traditional attempts search for single biomarkers—like pupil dilation, heart rate, or skin sweat. But the human body does not respond in lockstep: eyes react in milliseconds, while autonomic sweat and cardiovascular responses lag by seconds. Standard algorithms struggle to make sense of these misaligned speeds.
The AI Solution
This project reframes the problem as an asynchronous multimodal machine learning challenge. You will design and train multimodal transformer architectures and self-supervised models to fuse disparate physiological time series. The primary goal is to disentangle non-specific sensory arousal (e.g., sudden loud noises) from genuine cognitive listening fatigue (working memory depletion).
Methodology and facilities
You will use the world-class acoustic laboratories and calibrated listening booths at the Institute of Sound and Vibration Research (ISVR). You will work with rich, multi-channel physiological datasets (EEG, pupillometry, ECG, galvanic skin response, fNIRS) collected during speech-in-noise paradigms, building neural networks in PyTorch. The resulting algorithms will provide the foundation for closed-loop hearables and assistive listening devices that automatically adapt their signal processing when the user is struggling.
Training
You will receive comprehensive interdisciplinary training bridging modern machine learning and experimental hearing science:
• advanced technical & AI training: hands-on training in state-of-the-art deep learning architectures (multimodal transformers, self-supervised learning, sequence modelling) using PyTorch, alongside access and training on the University's High-Performance Computing cluster (Iridis)
• experimental and biosignal methods: specialist training in the world-class acoustic facilities of the Institute of Sound and Vibration Research (ISVR), covering calibrated audio reproduction, psychophysical speech testing, ethical human research governance, and synchronous multi-channel physiological data acquisition (EEG, pupillometry/eye-tracking, ECG, galvanic skin response)
• professional and academic development: full access to the Southampton Doctoral College training programme, including research data management, scientific writing, intellectual property, and public dissemination
• industry and network exposure: regular opportunities to present at international conferences (e.g., ICASSP, Interspeech, ISH) and interact with our collaborative network across consumer audio and hearing technology developers.
The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break.
The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
Potential supervisors
Lead supervisor
Professor Stefan Bleeck
Professor of Hearing Science & Technology
Research interests
• The intersection of hearing science, audiology, and signal processing
• Bio-inspired auditory modeling
• Speech intelligibility in noise
Supervisors
Dr Christoph Tremmel
Lecturer
Research interests
• brain-computer-interfaces
• electroencephalography
• biomedical engineering
Entry requirements
You must have a UK 2:1 honours degree or its international equivalent in one of the following:
• computer science
• Artificial Intelligence
• electrical and electronic engineering
• Data Science
• biomedical engineering
• or a relevant discipline
Essential skills:
• strong programming proficiency in Python
• hands-on experience with modern deep learning frameworks (primarily PyTorch)
• excellent written and verbal communication skills in English
• the ability to manage interdisciplinary research tasks independently
Desirable skills:
• practical experience processing and analysing physiological time-series or biosignals (e.g., pupillometry/eye-tracking, ECG/HRV, GSR/EDA)
• familiarity with transformer architectures, attention mechanisms, or self-supervised representation learning
• basic knowledge of acoustics, audio signal processing, or psychoacoustic experimental design
• experience working in Linux environments and with version control (Git)
Further training for all these will be provided.
Fees and funding
We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.
Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
For more information, please visit our postgraduate research funding pages.
How to apply
Apply now
You need to:
• choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences
• select Full time or Part time
• search for programme PhD Engineering & the Environment (7175)
• add name of the supervisor in section 2 of the application
Applications should include:
• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date
• English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences
For questions about applying, email our Doctoral College (doctoralcollege-admissions@soton.ac.uk).
Project leader
For an initial conversation, email Professor Stefan Bleeck (bleeck@soton.ac.uk).