PhD in Adapting Transformer Models for Defect Detection with Limited Data

16 Jan 2026
Job Information

Organisation/Company

Eindhoven University of Technology (TU/e)

Research Field

Computer science » Informatics
Computer science » Programming
Engineering » Computer engineering
Engineering » Electrical engineering

Researcher Profile

First Stage Researcher (R1)

Application Deadline

14 Feb 2026 - 22:59 (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

Join the NWO Perspectief FIND program and develop methods to adapt Transformer-based foundation models for defect detection where data is scarce and unlabeled. Explore few-shot learning, self-supervised adaptation, and synthetic data generation to enable robust, scalable AI in semiconductor and printing systems. Work with leading industry partners like Canon and help transform quality inspection in next-generation high-tech equipment!
Information
Industrial edge deployments—in semiconductor manufacturing, industrial printing systems, automotive radar, smart mobility cameras, and HealthTech—require on-device AI to ensure low latency, privacy, and resilience. Today’s Transformers models scale poorly and assume abundant cloud resources. The research program FIND aims to deliver architectural and algorithmic breakthroughs that enable foundation models to run predictably and efficiently on embedded processors and accelerators.
FIND is a research program funded by the Dutch government and industry that brings together 5 universities, 11 companies (startups to multinationals), and 2 knowledge institutes to develop foundation models (large AI models) for Dutch high‑tech industry, with strong emphasis on edge deployment, privacy, and timely decision‑making. Partners include ASML, NXP, Canon Production Printing, ASMPT, Technolution, Signify, Shell, Stryker, TNO, and others. A total of 12 PhDs will be employed on the FIND program covering topics from foundation model pre-training and multimodal adaptation to architectures and compression for edge deployment while targeting real-world validation in domains like HealthTech, smart industry, and autonomous mobility.
This PhD position focuses on adapting and fine-tuning Transformer-based foundation models for defect detection in high-tech manufacturing environments where only limited and largely unlabeled defect data is available. Current solutions typically rely on supervised CNN-based models trained on large labeled datasets, which fail when defects are rare, vary across machines, or when labeling is prohibitively expensive. These approaches lack flexibility and generalization, making them unsuitable for dynamic industrial settings with scarce and imbalanced data.
You will also explore few-shot learning, self-supervised adaptation, and multimodal integration techniques to overcome data scarcity and improve robustness. Unlike existing methods that depend on exhaustive annotation or handcrafted features, this research will leverage the rich representations of foundation models and develop strategies for zero-shot or few-shot adaptation. You will investigate domain adaptation, synthetic data generation, and cross-modal learning to enable models that generalize across defect types and machine configurations. This ensures scalable, accurate defect detection even in low-resource industrial contexts.
The resulting models will be validated in collaboration with a lead high-tech company, demonstrating how foundation models can transform quality inspection by reducing dependency on labeled data and enabling rapid adaptation to new defect patterns—closing the gap between AI capability and real-world manufacturing constraints.
Research group and company
This position is embedded in the Mobile Perception Systems (MPS) Lab and Electronic Systems (ES) group within the Electrical Engineering department at Eindhoven University of Technology (TU/e). The MPS lab and ES group have a strong history of collaborative research projects leading to real-world impact.
This PhD project is executed in close collaboration with Canon Production Printing which is a global leader in high-end digital printing, offering advanced hardware, software, and services aimed at professional and industrial-scale print environments.

Where to apply

Website

https://www.academictransfer.com/en/jobs/357774/phd-in-adapting-transformer-mod…

Requirements
Specific Requirements

• A master’s degree (or an equivalent university degree) in Computer Science, Electrical Engineering, Artificial Intelligence, or related background.
• Strong background in machine learning, computer vision, and deep learning.
• Knowledge of transformer architectures and foundation models.
• Experience with few-shot learning, self-supervised learning, or domain adaptation is a plus.
• Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow).
• Ability to work in an interdisciplinary team.
• 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.
• 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. Sander Stuijk, s.stuijk@tue.nl .
Visit our website for more information about the application process or the conditions of employment . You can also contact Kevin Caris, HR advisor, k.t.caris@tue.nl or +31 40 247 8835.
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/357774/

Work Location(s)

Number of offers available

1

Company/Institute

TU/e

Country

Netherlands

City

Eindhoven

Postal Code

5612AZ

Street

De Zaale

Geofield

Contact

City

Eindhoven

Website

http://www.tue.nl/

Street

De Rondom 70

Postal Code

5612 AP

STATUS: EXPIRED

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