多尺度图神经网络与微观力学结合的混凝土预测博士研究员
Multiscale graph neural networks with micromechanics for concrete prediction
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: • cover letter explaining your motivation and suitability for the project • research proposal (1 or 2 pages) • your CV (resumé) • 2 academic references • degree transcripts and certificates to date • English language qualification (if applicable)
- 最近申请截止
- 2027-08-31
- 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
- • cover letter explaining your motivation and suitability for the project
- • research proposal (1 or 2 pages)
- • 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
- 该项目将开发一个结合微观力学理论的分层图神经网络，用于预测混凝土的力学性能。研究将结合材料数据、数值模拟和人工智能，通过建立浆体、砂浆和混凝土的联动模型，跨尺度连接力学行为并评估其预测结果。核心挑战在于整合微观力学理论，研究均匀化方法以估计整体刚度，并模拟加载过程中的应力传递和界面损伤。
- 申请条件
- 申请人必须持有土木或机械工程、材料科学、计算机科学、数学或相关学科的英国2:1荣誉学位或其国际同等学历。具备将人工智能与混凝土力学结合的兴趣、独立工作及有效沟通协作的能力。编程经验、混凝土材料、固体力学、机器学习或有限元建模等知识和经验为加分项。非英语母语者必须满足大学的英语语言要求。
- 待遇
- 学校提供竞争性资助机会（Competition-based studentships），通常包含英国水平的学费和生活费津贴。地平线欧洲学费减免可自动为符合条件的优秀学生补足海外学费与英国学费的差额。总统奖学金（Presidential Bursaries）为顶尖申请者补足海外与英国学费差额。资金将按滚动方式发放。
- 申请方式
- 申请人需通过大学系统在线申请，选择研究型项目类型（2027/28学年，工程与物理科学学院），全职或兼职，搜索项目代码 PhD Engineering & the Environment (7175)，并在申请第二部分添加导师姓名。
- 材料清单
- 解释动机和项目契合度的求职信（cover letter）
- 1或2页的研究计划书（research proposal）
- 个人简历（CV / resumé）
- 2封学术推荐信
- 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
- 2027-08-31
- 学科
- 材料科学
- 合同类型
- 项目资助
- 本站收录
- 内容更新
- 导师
- Dr Ye Li
- 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
- english_ok
Applicants whose first language is not English must meet the University's English language requirements.
原文
View all current projects
Postgraduate research project
Multiscale graph neural networks with micromechanics for concrete prediction
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 Aug 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
Concrete properties depend on interactions between multiscale structures and their interfaces. This project will develop a hierarchical GNN by integrating micromechanics theory. Combining material data, numerical simulations and AI, the research will connect mechanical behaviour across scales and evaluate predictions of concrete mechanical properties.
Concrete may appear uniform, but its strength depends on how cement paste, aggregates, and their interfaces interact. Fine aggregate combines with paste to form mortar; coarse aggregate introduces another level of interaction. Thin regions around aggregate particles influence load transfer and where cracks begin to form. Predicting how these connected scales govern strength remains challenging, particularly when measurements of individual constituents are incomplete.
This project will complete and extend an emerging hierarchical graph neural network for concrete. You will develop linked models of paste, mortar and concrete to investigate how constituent properties and interfaces influence concrete mechanical properties. The central research challenge is to integrate micromechanics: the theory linking the behaviour of a composite to its constituents. You will investigate homogenisation to estimate overall stiffness from constituent properties, and model stress transfer and interface damage during loading.
These relationships will guide how the network combines information between scales. Experimental data and numerical simulations will support development and validation, including checks on intermediate material properties where measurements are available. Comparisons with conventional machine learning and mechanics models will assess whether the combined approach improves prediction and physical consistency. The model will help integrating civil engineering, material science and AI technologies.
You will:
• develop expertise in machine learning and computational mechanics through a concrete materials problem
• learn to connect material observations, physical theory and numerical models, and critically assess AI predictions
• build transferable skills for research careers in materials modelling, engineering software and AI-assisted design
You'll receive training in Python, machine learning and computational mechanics. Technical training will cover micromechanical models, numerical simulation and the transfer of material properties between scales. You'll learn how to combine experimental and simulated data, test the accuracy of model predictions, and assess whether they remain consistent with physical behaviour. Professional development will support scientific writing, research presentations and reproducible software development. You'll gain experience communicating across materials science, mechanics and AI, and explaining the strengths and limitations of the models you develop.
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
Dr Ye Li
PhD
Lecturer
Research interests
• High-Performance Marine Concrete
• Sustainable Cementitious Material and Concrete
• Fire Resistance of Concrete Materials and Structures
Entry requirements
You must have a UK 2:1 honours degree, or its international equivalent, in one of the following:
• civil or mechanical engineering
• materials science
• computer science
• mathematics
• a related discipline
Essential skills:
• an interest in connecting AI with concrete mechanics, and a willingness to develop skills across both areas
• ability to work independently, communicate clearly and collaborate effectively
Desirable skills:
• programming experience in Python or a comparable language
• knowledge of concrete materials, solid mechanics or machine learning
• experience with machine learning, finite element modelling or experimental data analysis
• research experience and clear scientific writing
Applicants whose first language is not English must meet the University's English language requirements.
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:
• cover letter explaining your motivation and suitability for the project
• research proposal (1 or 2 pages)
• 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, please email our Doctoral College (doctoralcollege-admissions@soton.ac.uk).
Project leader
For an initial conversation, email Dr Ye Li (Ye.Li@soton.ac.uk).