面向流体动力学与水力学的科学机器学习博士研究员
Scientific machine learning for fluid dynamics and hydraulics
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
- 2027-05-01
- 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
- 本项目基于物理信息神经网络（PINNs）开展流体动力学与水力学应用的研究，开发和优化用于洪水快速建模、替代纳维-斯托克斯求解器以及各种流固耦合模拟的PINNs模型。
- 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，专业背景包括机械工程、航空航天工程、土木工程、计算机科学、物理学、数学或相关学科，并具备基础编程技能。
- 待遇
- 提供竞争性资助机会（包括学费及生活费津贴），南安普顿大学的总统奖学金（Presidential Bursaries）及学校各学院提供的奖学金可覆盖顶尖申请者的学费和生活成本，同时提供高性能计算支持与全方位的博士培养培训。
- 申请方式
- 通过南安普顿大学在线系统申请，选择研究型项目（Research）、2027/28学年、工程与物理科学学院，搜索“PhD Engineering & the Environment (7175)”项目，并在申请表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
- 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
- 2027-05-01
- 学科
- 工程
- 合同类型
- 项目资助
- 本站收录
- 内容更新
- 导师
- Dr Sergio Maldonado
- 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
- is_phd
You will receive comprehensive training to ensure the successful completion of your PhD.
- english_ok
English language qualification (if applicable)
- bachelor_ok
You must have a UK 2:1 honours degree or its international equivalent
原文
View all current projects
Postgraduate research project
Scientific machine learning for fluid dynamics and hydraulics
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
1 May 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
Physics-Informed Neural Networks (PINNs) provide an innovative, efficient alternative to traditional methods employed in fluid dynamics simulations, delivering CFD-level of accuracy at significantly lower computational costs—thus reducing energy use and benefiting the environment.
Artificial Intelligence (AI) has become deeply integrated into modern society, offering immense benefits that often eclipse its limitations. Neural Networks (NNs), the most prevalent form of AI, typically demand vast training datasets and substantial computational power—resulting in high energy consumption and a considerable carbon footprint. Additionally, collecting and preparing data remains a major challenge that limits AI accessibility. However, many problems in physics and engineering are governed by well-established laws, often expressed as partial differential equations (PDEs). By incorporating this physical knowledge into neural networks, the need for large datasets can be greatly reduced—or even eliminated—along with much of the computational cost. These specialised networks are known as Physics-Informed Neural Networks (PINNs).
In this project, building on previous research by the host team, you will work towards the development and optimisation of PINNs for applications in various problems associated with fluid mechanics and hydraulics. Examples of applications include fast modelling of floods, alternative Navier-Stokes solvers and simulations of various fluid-structure interactions.
You will join a diverse, vibrant and growing PINNs research group at the University of Southampton, with access to one of the UK’s most powerful supercomputers, and support from a community of PhD students working in various aspects of Computational Fluid Dynamics and AI. You will also be part of a growing network of international collaborators.
You will receive comprehensive training to ensure the successful completion of your PhD. This includes access to dedicated support at Southampton for High Performance Computing and Computational Fluid Dynamics.
Additionally, the host research team—comprising several research students working on PINNs—will provide further guidance and collaboration opportunities.
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 Sergio Maldonado
PhD, MSc, BSc (Eng)
Associate Professor
Research interests
• Environmental fluid mechanics and hydraulics
• Dynamics of various fluid-particle (algae, plastics, etc.) interactions
• Use of AI (Physics Informed Neural Networks) in fluid dynamics
Entry requirements
You must have a UK 2:1 honours degree or its international equivalent in one of the following:
• mechanical engineering
• aerospace engineering
• civil engineering
• computer science
• physics
• mathematics
• or related disciplines
Essential skills:
• basic programming skills
Desirable skills:
• a strong foundation in fluid mechanics, hydraulics and/or neural networks
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 Dr Sergio Maldonado (s.maldonado@soton.ac.uk).