PhD in Multi-modal AI for UAV-based Structural Defect Analysis

17 Mar 2026
Job Information

Organisation/Company

Eindhoven University of Technology (TU/e)

Research Field

Engineering » Civil engineering
Engineering » Electrical engineering

Researcher Profile

First Stage Researcher (R1)

Application Deadline

16 Apr 2026 - 21:59 (UTC)

Country

Netherlands

Type of Contract

Temporary

Job Status

Not Applicable

Hours Per Week

36.0

Is the job funded through the EU Research Framework Programme?

Not funded by a EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Are you passionate about AI, multi-modal sensing, and resilient infrastructure? Would you like to integrate advanced AI techniques with heterogeneous UAV-based sensor data for the inspection and maintenance of critical transportation infrastructure, addressing real-world challenges faced by industry, governments, and society within the international STRUCTURE project?
Information
The PhD candidate will work within the international research project STRUCTURE in cooperation with industrial partners from the Netherlands, the United Kingdom, Belgium, Turkey, and Portugal. The project aims to automate and enhance inspection of transportation infrastructure through multi-modal sensing, autonomous UAV platforms, and advanced AI-based analysis. A central focus is the detection of defects in bridges and viaducts using X-ray, LiDAR, visual and acoustic data captured from UAVs.
The research will address the design of AI models capable of combining heterogeneous sensor modalities, including RGB, thermal, LiDAR, acoustic arrays, GPR, and X-ray backscatter, to create a unified and reliable representation of structural integrity. The work expands on TU/e’s contributions by developing algorithmic components for detection and classification of defects and anomalies across both surface and subsurface layers. This includes constructing robust feature extraction pipelines, attention-based fusion architectures, and deep learning models that accurately characterize cracks, voids, delamination, corrosion, and internal structural discontinuities. The PhD candidate will investigate Vision Language Models (VLMs), Multi-modal AI solutions, and 3D scene reasoning approaches, to achieve spatial understanding and cross-modal representation learning from heterogeneous sensor data, with the research not limited to these methods. This research will support semantic interpretation, defect localization, temporal reasoning, and predictive maintenance in complex inspection environments.
A second contribution involves predictive maintenance algorithms that integrate static data sources (such as geological maps, material properties, and usage profiles) with dynamic sensor measurements (including pressure, vibration, visual, acoustic, and X-ray signals). The PhD candidate will investigate temporal modelling, multimodal analysis, and risk progression modelling to forecast deterioration patterns and estimate the remaining useful lifetime of infrastructure components. The research also encompasses model compression and optimization for edge deployment on UAV-mounted processors to support real-time inference. The candidate will collaborate with industrial partners for real-world data acquisition and large-scale validation on operational bridges and viaducts.
Research group and company
The PhD student will be working in the AIMS laboratory of the Signal Processing Systems (SPS) group within the Department of Electrical Engineering at TU/e. The AIMS lab researches and develops AI models for systems equipped with sensors of multiple different modalities. We foster expertise in AI analysis of RGB, thermal, depth, LiDAR, acoustic, sonar and radar sensor data, with established research lines in 3D reconstruction, and Edge AI for resource-constrained deployments.

Where to apply

Website

https://www.academictransfer.com/en/jobs/359394/phd-in-multi-modal-ai-for-uav-b…

Requirements
Specific Requirements

• A master’s degree in Electrical Engineering, Computer Science, Artificial Intelligence or in a strongly related discipline.
• Experience with deep learning framework PyTorch or similar.
• Strong background in machine learning, image or signal processing.
• Knowledge of SotA models for multi-modality and scene understanding.
• Experience with multi-modal sensor data, such as X-ray, LiDAR, visual, acoustic, thermal, or GPR.
• Ability to work in an interdisciplinary team and interested in collaborating with industrial partners.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level).

Additional Information
Benefits

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities , scale P (min. € 3,059 - max. € 3,881).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process .
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• Unlimited access to the modern on‑campus TU/e Student Sports Center at an exceptionally affordable rate, for you and, if applicable, your partner.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

Selection process

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application.

Ensure that you submit all the requested application documents. We give priority to complete applications.
We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

Additional comments

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Dr.ir. Egor Bondarev, e.bondarev@tue.nl , Head of AIMS lab, and dr. Erkut Akdag e.akdag@tue.nl , PostDoc researcher in AIMS lab.
Visit our website for more information about the application process or the conditions of employment . You can also contact Floor de Groot, HR advisor, f.r.d.groot@tue.nl or +31 40 247 3040.
Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video .
Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Website for additional job details

https://www.academictransfer.com/359394/

Work Location(s)

Number of offers available

1

Company/Institute

TU/e

Country

Netherlands

City

Eindhoven

Postal Code

5612AZ

Street

De Zaale

Contact

City

Eindhoven

Website

http://www.tue.nl/

Street

De Rondom 70

Postal Code

5612 AP

STATUS: EXPIRED

• X (formerly Twitter)
• Facebook
• LinkedIn
• Whatsapp
• 
  More share options
  • E-mail
  • Pocket
  • Viadeo
  • Gmail
  • Weibo
  • Blogger
  • Qzone
  • YahooMail