机器学习在环境流体力学建模中的应用博士研究员
Machine Learning in environmental fluid dynamics modelling
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
- 2027-06-30
- 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
- 本项目旨在利用物理信息神经网络（PINNs）革新环境流体建模，结合流体力学、机器学习和世界级超级计算，开发从洪水模拟到可持续工程设计的创新解决方案。
- 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，专业背景包括物理学、工程学、应用数学或紧密相关学科，具备流体力学强大学术背景及对AI与流体力学交叉研究的热情。
- 待遇
- 竞争性资助项目通常为名列前茅的申请者提供英国本土水平的学费以及生活成本津贴，并提供顶尖计算资源和充满支持的研究社区。
- 申请方式
- 申请人需通过南安普顿大学系统选择2027/28学年工程与物理科学学院的PhD Engineering & the Environment (7175)全日制或非全日制项目，并在申请表第二部分添加导师姓名。
- 材料清单
- 个人简历 (CV / resumé)
- 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
- 2027-06-30
- 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 Gustavo De Almeida, Dr Sergio Maldonado
- 来源
- 南安普顿大学博士研究项目招生 · 最近核对 2026-10-17
- 详情核验
判定依据（原文摘录）
- bachelor_ok为true
You must have a UK 2:1 honours degree or its international equivalent
- english_ok为true
We welcome applications from talented students around the world and encourage individuals from diverse backgrounds to apply.
原文
View all current projects
Postgraduate research project
Machine Learning in environmental fluid dynamics modelling
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
30 Jun 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
Join an exciting PhD at the University of Southampton, where you will harness cutting-edge Physics-Informed Neural Networks to transform environmental flow modelling. Combining fluid mechanics, machine learning and world-class supercomputing, you will develop innovative solutions to real-world challenges, from flood simulations to sustainable engineering design, within a collaborative, internationally renowned research community.
Are you passionate about leveraging artificial intelligence to address pressing environmental challenges? Join our innovative team at the University of Southampton as we explore groundbreaking applications of Physics-Informed Neural Networks (PINNs) in solving complex environmental flow problems.
This exciting PhD project focuses on using advanced machine-learning techniques to simulate environmental flows, including the 2D Shallow Water Equations, pivotal for understanding flood dynamics, to guide engineering design of submerged infrastructure, and in water resource management.
The project builds on our latest research in PINNs, merging fluid mechanics with state-of-the-art AI to achieve faster, more accurate, and robust solutions for real-world environmental challenges.
As part of this research, you will join a vibrant community of PhD students working on cutting-edge tools at the intersection of machine learning and fluid dynamics. Our Water and Environmental Research Group is a world leader in computational modelling and environmental science, providing an inspiring and collaborative environment to help you excel in your studies.
In addition, you will have access to one of the fastest supercomputing facilities in the UK, enabling high-performance simulations and data analysis to push the boundaries of current environmental flow modelling techniques.
We welcome applications from talented students around the world and encourage individuals from diverse backgrounds to apply. Funding is awarded competitively based on academic excellence.
What we offer:
• a dynamic and supportive research community at a top UK university
• access to cutting-edge computational resources to accelerate your research.
Embark on a journey where machine learning meets environmental engineering, and contribute to a sustainable future with AI-driven solutions. Apply now to make your mark in this transformative field!
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
GDA
Dr Gustavo De Almeida
Associate Professor
Supervisors
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:
• physics
• engineering
• applied mathematics
• or a closely related discipline
A Master's degree in a relevant subject is desirable.
Essential skills and experience:
• strong academic background in fluid mechanics
• passion for interdisciplinary research combining AI and fluid dynamics
Desirable skills:
• programming experience
• familiarity with numerical methods
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 Gustavo de Almeida (g.dealmeida@soton.ac.uk).