隧道不确定性感知的状态评估博士研究员

Doctoral Position in Uncertainty-Aware Condition Assessment of Tunnels

Eidgenössische Technische Hochschule Zürich ETH · 瑞士 · Zurich

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。

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研究内容
该博士研究员职位聚焦于将异构检测和监测证据转化为数字孪生中隧道结构及机电和给排水系统的不确定性感知的状态指标,属于跨学科SNSF项目“PRISM-T”的一部分。
申请条件
申请者须持有土木工程、测绘、计算机科学或相关领域的硕士学位,具备机器学习、计算机视觉或3D点云处理以及概率建模和贝叶斯推断的坚实背景,具备极佳的编程技能(首选Python),熟练掌握英语书面和口头表达。
待遇
苏黎世联邦理工学院(ETH Zürich)提供全职(100%)、固定期限的科研助理/博士生岗位,工作地点位于瑞士苏黎世。
申请方式
申请者须通过在线申请门户提交申请,截止日期为2026年11月9日。
材料清单
  • 包含对项目中潜在研究想法的兴趣信(Letter of interest)
  • 个人简历(CV,若适用含出版物列表以及至少两名推荐人的联系方式)
  • 所有修读大学课程的成绩单以及学位证书

由 gemini-2.5-flash-lite 生成,博士岗判定置信度 100%。

岗位信息

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原帖导师候选(待核验)
Bryan T. Adey
来源
瑞士高校与研究机构官方招聘 · 最近核对 2026-10-14
详情核验
判定依据(原文摘录)
  • 这是一个博士生职位
    has an opening for a doctoral student.
  • 要求英语熟练
    Proficiency in written and spoken English is required
  • 要求持有硕士学位
    A Master’s degree in civil engineering, geomatics, computer science, or a related field
原文

Doctoral Position in Uncertainty-Aware Condition Assessment of Tunnels

100%, Zurich, fixed-term

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The Chair of Infrastructure Management, led by Professor Dr. Bryan T. Adey within the Institute of Construction and Infrastructure Management of the Department of Civil, Environmental and Geomatic Engineering, has an opening for a doctoral student. This position focuses on transforming heterogeneous inspection and monitoring evidence into uncertainty-aware condition indicators for tunnel structures and mechanical, electrical, and plumbing systems within a digital twin. This position is part of an interdisciplinary SNSF project in collaboration with the Free University of Bozen-Bolzano, the Hagerbach Test Gallery, and the EuroTube Foundation.

The preferred starting date is 1 February 2027, although other dates are negotiable.

Project background

Tunnels are critical links in transport networks, and many Swiss road and rail tunnels are ageing while being subject to increasing traffic loads, climate-related stressors, and evolving safety requirements. Their maintenance involves two interdependent asset domains, namely structural elements, such as lining, portals, and drainage, and mechanical, electrical, and plumbing systems, such as ventilation, lighting, and fire detection. In current practice, these domains are managed largely in isolation, inspections remain heavily manual, and evidence from different campaigns, sensors, and specialists is fragmented across separate reports and databases. Interventions are therefore often planned using disjointed evidence and conservative heuristics, even where substantial data exist.

The project Predictive, Resilient, Integrated Systems for Management of Tunnel (PRISM-T), funded by the SNSF, addresses this challenge through an integrated digital twin workflow for tunnel maintenance decision support. Heterogeneous inspection and monitoring evidence is transformed into uncertainty-aware condition indicators, which are used to update risk, to assess whether additional information is worth collecting, and to derive executable intervention programs. Extended reality (XR) is used to validate decisions and to guide inspection and maintenance work on site.

The workflow is developed and validated in the Tunnel Digitalisation Centre of the Hagerbach Test Gallery in Flums, and its transferability is tested at the DemoTube facility of the EuroTube Foundation in Dübendorf. Regular field work at both sites and close collaboration with the partner team in Bolzano form an integral part of the doctoral research.

Within this project, the doctoral position addresses the evidence layer of the workflow, in which inspection images, 3D laser scans, and sensor data are transformed into condition indicators that form the basis for the risk updating and intervention planning carried out in a companion doctoral position.

Job description

The successful candidate will develop methods to extract defect and deterioration indicators from inspection images, video, sensor data, and periodic 3D laser scans, including domain adaptation for tunnel environments and time-lapse change detection. Evidence from multiple sources will be fused into asset-level condition indicators using a hierarchical Bayesian framework, and uncertainty models and evidence quality flags will be developed for each source. The candidate will contribute to defining the condition indicator types in the digital asset model, to integrating the indicators into the digital twin prototype, and to inspection and validation campaigns at the Hagerbach Test Gallery and the EuroTube Foundation.

Profile

• A Master’s degree in civil engineering, geomatics, computer science, or a related field

• A strong background in machine learning, computer vision, or 3D point cloud processing, as well as in probabilistic modelling and Bayesian inference

• Very good programming skills, preferably in Python

• Familiarity with digital twins, building information modelling (BIM) and IFC, sensor data processing, or tunnel inspection would be considered an advantage

• Willingness to carry out field work in underground environments and to collaborate within an interdisciplinary and international team

• Proficiency in written and spoken English is required, and knowledge of German is regarded as an asset

Workplace

Workplace

We offer

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

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We value diversity and sustainability

In line with our values , ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future .

Curious? So are we.

We look forward to receiving your online application by 9 November 2026 including the following documents:

• Letter of interest including your ideas of potential research in the project

• A curriculum vitae (with list of publications, if applicable, and contact information of at least two referees)

• Grades of all university courses taken as well as diplomas

Screening of applications starts on 9 November 2026. Applications will be accepted until the position is filled.

Further information about the Institute of Construction & Infrastructure Management can be found on our website . Questions regarding the position should be directed to Ms. Nathalie Dietrich, dietrich@ibi.baug.ethz.ch (no applications).

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.

About ETH Zürich

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

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