现代电力系统数据驱动建模与智能管理博士研究员
Data-driven modelling and intelligent management for modern electrical power systems
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 Electronic & Electrical Engineering (7092) • 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
- 2027-02-28
- 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
- 本项目旨在开发数据驱动的资产关键性排序和维护优化方法，通过将机器学习与运营、状态监测及环境数据相结合，研究多应力退化，预测资产故障风险并优化电力系统可靠性。
- 申请条件
- 申请者需持有电气或电子工程专业的英国 2:1 荣誉学士学位或国际同等学历。
- 待遇
- 学校为表现优异的申请者提供竞争性总统奖学金或院系奖学金，通常涵盖英国本土学费及生活津贴，资助以滚动方式发放。
- 申请方式
- 申请人需通过学校系统选择 2027/28 学年工学与物理科学学部、全日制或兼读制，搜索并申请 PhD Electronic & Electrical Engineering (7092) 课程，并在申请表第二部分添加导师姓名，截止日期为 2027 年 2 月 28 日。
- 材料清单
- 研究计划书
- 简历
- 2 封学术推荐信
- 迄今为止的学位成绩单与证书
- 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
- 2027-02-28
- 学科
- 工程
- 合同类型
- 项目资助
- 本站收录
- 内容更新
- 原帖导师候选（待核验）
- Huajie Yi
- 来源
- 南安普顿大学博士研究项目招生 · 最近核对 2026-10-17
- 详情核验
判定依据（原文摘录）
- is_phd为true
Type of degree Doctor of Philosophy
- bachelor_ok为false
A UK 2:1 honours degree, or its international equivalent, in electrical or electronic engineering.
- supervisor_name为Huajie Yi
Lead supervisor Dr Huajie Yi
原文
View all current projects
Postgraduate research project
Data-driven modelling and intelligent management for modern electrical power systems
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
28 Feb 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
This project aims to develop data-driven asset criticality ranking and maintenance optimisation methods. By integrating machine learning with operational, condition-monitoring, and environmental data, it investigates multi-stress degradation, predicts asset failure risks, and prioritises critical assets to improve maintenance efficiency and power system reliability.
Effective asset management in electrical power systems is essential for ensuring operational reliability, minimising downtime, and optimising maintenance costs. However, traditional maintenance strategies often overlook the combined effects of environmental and operational stresses on asset degradation, limiting the accuracy of asset risk assessment and maintenance prioritisation.
This project aims to develop a data-driven framework for asset condition assessment, failure risk prediction, criticality ranking, and maintenance optimisation. Particular attention will be given to understanding how multiple stress factors influence asset health and long-term reliability.
The research will combine laboratory-based experiments with the analysis of available datasets, published research findings, and relevant environmental and operational information. Experimental investigations will provide insights into degradation mechanisms, while data analysis and modelling will support the assessment of asset performance and failure risks.
Expected outcomes include improved understanding of asset degradation mechanisms, more accurate risk assessment, systematic asset criticality ranking, and optimised maintenance strategies, supporting proactive decision-making and enhanced power system reliability.
You'll join an interdisciplinary and diverse academic team in the Electrical Power Engineering research group that will support you in expanding your transferrable skills, such as critical review of the literature, academic writing and publishing, as well as collaborating with your team members and organising activities. You'll acquire hands-on experience with testing facilities and operate state-of-the-art equipment in the Tony Davies High Voltage Lab (TDHVL) as part of your experimental research.
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 Huajie Yi
Senior Teaching Fellow
Research interests
• Power assets condition monitoring, assessment and management
• Power assets data analysis
Entry requirements
A UK 2:1 honours degree, or its international equivalent, in electrical or electronic engineering.
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 Electronic & Electrical Engineering (7092)
• 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 Huajie Yi (H.Yi@soton.ac.uk).