Internship | Situation understanding by neurosymbolic reasoning

We have an ambition towards higher levels of understanding the situations that are shown in images. For instance, a few people that form a group, which can be seen by whether they are looking at each other. Another example is that one person carries a bag and gives it to another person. We want to define such situations by first-order logic (symbolic predicates), and analyze them by recognizing the symbols in the image by deep learning models. The worlds of symbolic logical statements and machine/deep learning can be connected by a recent method called neurosymbolic programming. We want to explore this method for situation understanding.

We hypothesize that neuro-symbolic programming can be very valuable for situation understanding. That is, to define a situation by a particular combination of objects, e.g., a gathering involves multiple people standing close together (symbolic statement), measured by detections of the persons in the scene and features that can be pooled from them (neural network).

In the original paper, https://scallop-lang.github.io, it is possible to define the sum of two digits (symbolic statement), where each digit is perceived by an image (neural network). Together they form a neurosymbolic program. From the high-level definition of the sum, any mistakes by the neurosymbolic program are measured by a loss, which can backpropagate all the way to the digit neural network, to improve on the task of sum prediction.

Situation understanding is more complex. Consider group formation: a loss may promote that the persons’ features indeed indicate that they are facing at each other, i.e., that they are a social group. The goal of your research is to explore neuro-symbolic programming combined with deep learning for situation understanding.

You are in the final stages of your master's degree in artificial intelligence, computer science, physics, mathematics, electrical engineering, robotics, systems and control engineering, or a similar degree. You have some experience in computer vision, artificial intelligence and deep learning.

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.

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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.