Internship | Unsupervised deep learning methods to extract settlement data from images of masonry facades


Den Haag

Education type

university (wo)


Internship and graduation project

Hours a week

Fulltime – 40


Apply now


Uneven settlements of buildings are an important cause for cracks in masonry buildings. The assessment of the current state of the building is thus served by accurate information on the settlement profiles at building level. Combined with damage prediction models, this information can provide valuable insight on what to expect in the near future and in the long term. Settlements of buildings are traditionally measured using GPS, geodetic levelling or borehole extensometers. Although valuable, these approaches are labor intensive, time-consuming, expensive and difficult to be installed over wide regions. Therefore, innovative and cost-effective techniques for extracting building deformations are needed.

What will be your role?

This thesis aims to explore the utility of unsupervised deep learning (DL) techniques to extract settlement data from a large dataset of unannotated images of masonry facades. Such a DL-based approach has the potential to overcome all of the above limitations of the traditional approaches. Using unsupervised methods allows us to overcome the annotation burden usually imposed by supervised methods.

You will:
  • Continue and extend the work of Asset Hub and TNO researchers;
  • Work closely with experts in deep learning, computational physics and structural engineering at Asset Hub and TNO;
  • Review the most recent literature on supervised and unsupervised learning;
  • Develop a method to extract useful information out of a large dataset of unlabeled data.
  • Compare the accuracy of the DL-based approach with real measurement data;
  • Learn how to perform, document, and present your own research.

Conditional on good progress, we expect to publish one international journal paper based on the results of the MSc thesis. Your work will be embedded into an ongoing research project at TNO and you will jointly work with Asset Hub and TNO researchers rather than be left alone with your topic.

How do you want to contribute to tomorrow's world? How big can your impact be? Come and work at TNO and envision it.

What we expect from you

  • You are in the final stages of your master’s degree in artificial intelligence, computer science, physics, mathematics, electrical engineering, control engineering, or a similar degree and preferably have some track record in the field of computer vision and/or image processing.
  • You are pragmatic and focused on making things work. Next to technical expertise, we value communication skills and a results-driven attitude to add value to our clients and the Dutch society as a whole.
  • You have at least three of these skills (feel free to apply if you only have two of them but you are confident that you will pick up the rest):
  • You are familiar with the basics of deep learning and machine learning, e.g. neural networks, cross-validation. You have fitted and evaluated at least one neural network and you are familiar with the basic concepts such as activation function, fully connected layers, loss function, and automated differentiation.
  • You have demonstrated skills in at least one computer language, preferably with a focus on numerical computation, e.g. Matlab, Python, Julia, or R .
  • You have experience with at least one deep learning framework, e.g. TensorFlow, PyTorch.
  • You are proactive and eager to learn new concepts and develop new skills.

What you’ll get in return

You want to work on the precursor of your career; a work placement gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It’s not just looking that interests us; you and your knowledge are essential to our innovation. That’s why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your work placement and be given the scope for you to get the best out of yourself. Naturally, we provide suitable work placement compensation.

TNO as an employer

At TNO, we innovate for a healthier, safer and more sustainable life. And for a strong economy. Since 1932, we have been making knowledge and technology available for the common good. We find each other in wonder and ingenuity. We are driven to push boundaries. There is all the space and support for your talent and ambition. You work with people who will challenge you: who inspire you and want to learn from you. Our state-of-the-art facilities are there to realize your vision. What you do at TNO matters: impact makes the difference. Because with every innovation you contribute to tomorrow’s world. Read more about TNO as an employer.

At TNO we encourage an inclusive work environment, where you can be yourself. Whatever your story and whatever unique qualities you bring to the table. It is by combining our unique strengths and perspectives that we are able to develop innovations that make a real difference in society. Want to know more? Read what steps we are taking in the area of diversity and inclusion.

The selection process

After the first CV selection, the application process will be conducted by the concerning department. TNO will provide a suitable internship agreement. If you have any questions about this vacancy, you can contact the contact person mentioned below.

For this internship vacancy it is required that the AIVD issues a security clearance (VGB) after conducting a security screening. Take into account that this process may take about 8 weeks. If you have been abroad for more than 6 consecutive months, or if you do not have the Dutch nationality, it may take longer. Please visit for more information the AIVD website.

Due to Covid-19 and the consequent uncertainties and restrictions, students who are not residing in the Netherlands may currently not be able to start an internship or graduation project at TNO.

Has this job opening sparked your interest?

Then we’d like to hear from you! Please contact us for more information about the job or the selection process. To apply, please upload your CV and covering letter using the ‘apply now’ button.

Contact: Pieter Piscaer
Phone number: [email protected]

Note that applications via email and third party applications are not taken into consideration.


Apply now