生物医学信号认知与情感状态的可靠检测博士研究员
Reliable detection of cognitive and affective states from biomedical signals
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 Computer Science (7089) • add name of the supervisor in section 2 of the application Applications should include: • research proposal • your CV (resumé) • 2 academic references • degree transcripts and certificates to date • English language qualification (if applicable)
- 最近申请截止
- 2026-12-18
- 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
- • research proposal
- • 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
- 该项目旨在研究生物医学信号（特别是脑电图EEG）中的时间结构如何对分析造成无意识偏差并夸大性能估计，结合信号处理、统计建模与机器学习方法提高生物医学数据分析的可靠性与可重复性。
- 申请条件
- 申请者必须持有计算机科学、工程学、数据科学、生物医学工程、心理学、认知科学或密切相关学科的英国2:1荣誉学士学位或国际同等学历；具备编程经验（如Python、MATLAB、R等）、统计或定量数据分析经验，并对生物医学信号感兴趣。
- 待遇
- 提供跨数据科学、信号处理、神经科学和以人为中心的技术的跨学科培训，有机会与学术和工业界合作伙伴合作，并通过国际会议和期刊发表成果。学校提供一系列针对英国及国际学生的资助机会（如竞争性总统奖学金和学院奖学金，通常涵盖英国本土学费及生活费津贴）。
- 申请方式
- 申请人需选择研究项目类型（2027/28学年，工程与物理科学学院），全职或兼职，搜索PhD Computer Science (7089)项目，并在申请的第二部分添加导师姓名。
- 材料清单
- 研究计划 (research proposal)
- 简历 (CV / resumé)
- 2封学术推荐信 (2 academic references)
- 至今的学位成绩单和证书 (degree transcripts and certificates to date)
- 英语语言资格证明（如适用） (English language qualification (if applicable))
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
- 2026-12-18
- 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
- Dr Christoph Tremmel
- 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
- english_ok
Applicants whose first language is not English must meet the University's English language requirements (IELTS 6.5 overall with a minimum of 6.0 in each component).
- bachelor_ok
You must have a UK 2:1 honours degree, or its international equivalent, in one of the following:
原文
View all current projects
Postgraduate research project
Reliable detection of cognitive and affective states from biomedical signals
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
18 Dec 2026
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
Biomedical signals such as electroencephalography (EEG) are widely used to human cognition, physiology, and brain-computer interfaces. Yet their temporal structure can unintentionally bias analyses and inflate performance estimates. This project will investigate these effects and develop practical approaches to improve the reliability and reproducibility of biomedical data analysis.
Biomedical recordings contain noise, variability, and temporal dependencies arising from both physiological processes and measurement systems. These characteristics can influence statistical analyses and machine learning models throughout the analysis pipeline, leading to results that may not generalise to real-world applications. Although temporal dependencies are well understood in signal processing, their impact is not always systematically assessed across experimental design, data analysis, and machine learning workflows.
In this project, you will combine signal processing, statistical modelling, and machine learning methods to investigate how temporal structure influences conclusions drawn from biomedical data, with a particular focus on EEG. Using real-world datasets, you will evaluate the effects of temporal leakage on statistical inference and predictive modelling, compare approaches for mitigating these effects, and develop practical guidelines for robust and reproducible analysis. The project offers opportunities to work at the interface of biomedical engineering, data science, and human-centred technologies, with potential collaborations across academia and industry.
You will:
• characterise temporal dependencies and autocorrelation in biomedical signals, with a particular emphasis on EEG
• quantify how temporal structure influences statistical inference, predictive modelling, and the identification of human cognitive and physiological states
• evaluate and compare approaches for mitigating temporal effects across:
• experimental design (for example task timing and trial structure)
• signal processing (for example filtering, segmentation, and decorrelation)
• statistical and machine learning models that explicitly account for temporal dependencies;
• assess current practices in academia and industry and develop practical guidelines for robust and reproducible biomedical time-series analysis
You will gain experience in:
• biomedical signal processing and EEG analysis
• time-series modelling and statistical inference
• machine learning and reproducible data analysis
• experimental research using real-world biomedical datasets
The project provides interdisciplinary training across data science, signal processing, neuroscience, and human-centred technologies. You will have opportunities to collaborate with academic and industrial partners and to disseminate your work through international conferences and journal publications.
The School of Electronics and Computer Science 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
Dr Christoph Tremmel
Lecturer
Research interests
• brain-computer-interfaces
• electroencephalography
• biomedical engineering
Supervisors
Dr Nathan Huneke
MBChB MRes PhD MRCPsych
NIHR Academic Clinical Lecturer
Research interests
• Experimental medicine
• Neuroimaging
• Placebo and nocebo effects
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
Entry requirements
You must have a UK 2:1 honours degree, or its international equivalent, in one of the following:
• computer science
• engineering
• data science
• biomedical engineering
• psychology
• cognitive science
• a closely related subject
Essential skills:
• experience in programming (for example Python, MATLAB, R, or a similar language)
• experience with statistical or quantitative data analysis
• an interest in biomedical signals, time-series data, and research methods
• experience in one of the following areas:
• technical pathway: signal processing and machine learning
• behavioural science pathway: experimental design and quantitative analysis of behavioural or physiological data
Desirable skills:
• experience with EEG, physiological signal analysis, or machine learning
• experience conducting research involving human participants
• evidence of independent research activity, such as a dissertation, research project, publication, or relevant industrial R&D experience
Applicants whose first language is not English must meet the University's English language requirements (IELTS 6.5 overall with a minimum of 6.0 in each component).
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 Computer Science (7089)
• add name of the supervisor in section 2 of the application
Applications should include:
• research proposal
• 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 Dr Christoph Tremmel (Christoph.Tremmel@southampton.ac.uk).