X射线断层扫描图像高效重建的深度平衡机器学习模型博士研究员
Deep equilibrium machine learning models for the efficient reconstruction of X-ray tomographic images
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
- 2027-01-29
- 全部申请截止
- （Europe/London）
- 资助原文
- Fees and funding Potential funding to support this position will be available to the strongest candidates through the Faculty of Engineering and Physical Sciences graduate school studentship programme. This funding will be awarded on a highly competitive basis. We also 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. 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
- 本项目旨在开发先进的机器学习工具（特别是深度平衡模型），将真实的X射线测量数据转化为无伪影的3D图像，工作结合人工智能、优化理论、X射线物理、应用数学与计算机科学，并有机会与南安普顿大学µ-VIS X射线计算断层扫描中心合作。
- 申请条件
- 申请者需持有英国2:1荣誉学士学位或其国际同等学历（本科/优异本科学位要求）。
- 待遇
- 通过工程与物理科学学院研究生院学生计划向最优秀的候选人提供潜在竞争性资助，通常涵盖英国本土学费及生活费津贴，具体取决于竞争结果；同时也提供面向英国及国际学生的多种资助机会。
- 申请方式
- 申请人需通过网申系统进行申请，选择研究型项目（Research）、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)专业，并在申请的第二部分添加导师姓名。
- 材料清单
- 个人简历 (CV)
- 2封学术推荐信
- 截至目前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
- 2027-01-29
- 学科
- 计算机科学
- 合同类型
- 项目资助
- 本站收录
- 内容更新
- 导师
- Thomas Blumensath
- 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）
- 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
Deep equilibrium machine learning models for the efficient reconstruction of X-ray tomographic images
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
29 Jan 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
In this project you will develop advanced machine learning tools to convert real X-ray measurement data into artefact free 3D images. The work draws on a range of ideas from artificial intelligence, optimization, X-ray physics, applied mathematics and computer science.
Machine learning has revolutionised many scientific fields and has become an increasingly useful tools in a wide range of image processing applications; including in X-ray tomography. X-ray tomography (XCT) is an imaging technique that utilises X-ray radiation to generate a three-dimensional image of internal object structures. The technique is thus used routinely in medical diagnostics, security screening, industrial non-destructive testing as well as scientific investigations.
In many X-ray tomography applications, constraints on the imaging process mean that we are often only able to collect limited X-ray measurements, which can lead to significant image noise and artefacts. Many advanced machine learning methods have thus been proposed to reduce these errors. In this project, you will be exploring the use of the recently introduced deep equilibrium models, which are advanced artificial intelligence tools that combine ideas from optimisation, inverse problems and deep learning. Your work will focus on the use of these methods in realistic X-ray imaging settings with the goal to develop tools that can be readily applied to large real datasets.
To achieve these goals, several challenges need to be addressed, including the development of efficient computational methods to cope with realistically sized image data as well as the limits imposed by the lack of training data in many settings.
Whilst the project will be predominately computational, there will also be the chance to work closely with the University of Southampton’s dedicated X-ray Computed Tomography (X-CT) centre “µ-VIS”, which is part of the UK’s National facility for XCT. The centre houses some of the UK’s largest micro-focus CT scanning systems with the capability to unveil sub-surface information from an extremely wide range of materials, components and structures. With strong links between both research and industry, the centre is used for an extensive list of applications, which will offer many opportunities to apply your innovations directly to a host of relevant scientific and industrial imaging challenges.
Potential funding to support this position will be available to the strongest candidates through the Faculty of Engineering and Physical Sciences graduate school studentship programme. This funding will be awarded on a highly competitive basis.
Formal and informal training is available on a wide range of relevant scientific areas, which will be complemented with a programme in transferable skills training to prepare you for a wide range of potential careers.
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
Professor Thomas Blumensath
Professor
Research interests
• I develop and study advanced algorithms that can solve challenging inverse problems by efficiently exploiting complex prior information. Using techniques from mathematics, statistics and machine learning, my work concentrates primarily on problems in x-ray tomographic image reconstruction and modelling.
• I work closely with state-of-the-art imaging facilities (µ-VIS, the National Research Facility in Lab-based XCT, the UK’s synchrotron facility at the Diamond Light Source, and ISIS neutron imaging beamline) to find practical solutions to a range of important scientific problems from plant science to manufacturing.
• My research interests cover areas such as: Theoretical and computational methods for Signal and Image Processing (Machine Learning, Compressed Sensing, Statistical Signal and Image Processing, Quantum Computing, Inverse Problems, Optimisation, X-ray Tomographic Imaging); Advanced tomographic imaging strategies: (limited angle tomography and laminography, Spectral X-ray imaging, Stereo and extreme limited view tomography); Efficient computational methods for tomographic reconstruction, including GPU acceleration, distributed computation and advanced optimisation strategies, Constrained optimisation for ill-conditioned and underdetermined tomographic inverse problems, Applications of X-ray tomography to the inspection of manufactured components, Multimodal tomographic imaging
Supervisors
RB
Dr Richard Boardman
PhD, MInstP
Principal Enterprise Fellow
Entry requirements
A UK 2:1 honours degree, or its international equivalent.
A very good undergraduate degree; at least a 1st class honours degree, or its international equivalent.
Fees and funding
Potential funding to support this position will be available to the strongest candidates through the Faculty of Engineering and Physical Sciences graduate school studentship programme. This funding will be awarded on a highly competitive basis.
We also 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.
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
If you have a general question, email our Doctoral College (doctoralcollege-admissions@soton.ac.uk).
Project leader
For an initial conversation, email Professor Thomas Blumensath (Thomas.Blumensath@soton.ac.uk).