混合湍流建模：架起物理与数据的桥梁博士研究员
Hybrid turbulence modeling: bridging physics and data
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
- 2027-08-31
- 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
- • 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
- 本项目开创性地将深度学习应用于湍流建模，重点关注壁湍流，结合卷积神经网络（CNN）、生成对抗网络（GAN）和物理信息方法开发动态预测模型，以克服现有局限性并推进能源、交通和生物医学应用中的计算设计。
- 申请条件
- 申请人需持有英国 2:1 荣誉学士学位或其国际同等学历；具备流体力学、湍流、数据分析和 Python 方面的知识为佳。
- 待遇
- 学校为英国及国际学生提供一系列资助机会，竞争性学生奖学金通常涵盖英国本土水平的学费以及生活费津贴。
- 申请方式
- 申请人需选择研究型（Research）项目类型、2027/28学年、工程与物理科学学院，选择全职或兼职，搜索 PhD Engineering & the Environment (7175) 项目，并在申请的第二部分添加导师姓名后通过网申系统提交。
- 材料清单
- 简历（CV）
- 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
- 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）
- Dr Temistocle Grenga, Professor Edward Richardson, Professor Bharath Ganapathisubramani
- 来源
- 南安普顿大学博士研究项目招生 · 最近核对 2026-10-24
- 详情核验
判定依据（原文摘录）
- is_phd
Type of degree Doctor of Philosophy
- english_ok
English language qualification (if applicable)
- bachelor_ok
A UK 2:1 honours degree, or its international equivalent.
原文
View all current projects
Postgraduate research project
Hybrid turbulence modeling: bridging physics and data
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
This project pioneers deep learning for turbulence modeling, focusing on wall-bounded flows. By combining convolutional neural networks (CNNs), generative adversarial networks (GANs), and physics-informed methods, it aims to develop dynamic predictive models that overcome current limitations. The research supports scalable, accurate simulations of multi-scale phenomena, advancing computational design across energy, transport, and biomedical applications.
This project explores the cutting edge of artificial intelligence for turbulence modelling in turbulent flows, particularly in wall-turbulent flows. Using advanced deep neural networks, the research aims to create hybrid predictive models that combine theoretical insights with data-driven approaches that reliably simulate turbulent flows. While convolutional neural networks (CNNs) and generative adversarial networks (GANs) have shown promise in capturing complex flow structures (e.g., reverse energy cascade), they also come with challenges, including the need for extensive, high-resolution training data and limitations in generalizing to new conditions. The goal is to develop predictive hybrid models for the computational design.
This project will push the boundaries of current methods, addressing specific limitations: CNNs’ difficulty with high-frequency flow features and GANs’ computational demands. The research will pioneer the use of physics-informed loss functions and reinforced learning in a hybrid framework, enhancing model accuracy and applicability. The ultimate goal is to contribute models that can guide the design and development of fluid systems by predicting multi-physics, multi-scale phenomena, critical to advancing technologies in any field such as sustainable energy, transport or medical.
Key project tasks include:
• creating specialized training datasets
• optimizing network architectures for efficiency
• exploiting the inclusion of physical constraints
• integrating into CFS solvers
• validating models in realistic settings
This work will also focus on scaling solutions to Exascale computing, bringing new possibilities to turbulence simulations.
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 Temistocle Grenga
Associate Professor
Research interests
• Numerical simulations
• Hydrogen combustion
• Turbulence modeling
Supervisors
Professor Edward Richardson
Professor
Research interests
• Thermal energy systems for power and propulsion.
• Turbulent combustion simulation, theory and modelling.
• Dispersion and explosion of alternative fuels and other pollutant releases.
Professor Bharath Ganapathisubramani
Professor of Experimental Fluid Mechanics
Entry requirements
A UK 2:1 honours degree, or its international equivalent.
Knowledge of the following areas is desirable:
• fluid dynamics
• turbulence
• data analysis
• python
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, please email our Doctoral College (doctoralcollege-admissions@soton.ac.uk).
Project leader
For an initial conversation, email Dr Temistocle Grenga (T.Grenga@soton.ac.uk).