Internship | Computer Vision in Limited Data Real-World Settings

TNO; Delft; On-site; EUR 6,200 - 7,500; Full time

Benefits offered by this job

Internship stipend €615

Travel expense support

Jong TNO membership

Leave allowance 8h/mo

Job summary

TNO offers an internship focused on data-efficient computer vision with impact on infrastructure data collection and societal applications. You will explore self-supervised learning, few-shot learning, vision-language models, and foundation models, shaping a concrete research question with your supervisor.

Three options are available: predictive maintenance from drone imagery, asset extraction from street-view imagery, and video understanding for process monitoring.

Qualifications

• Background in Artificial Intelligence, Computer Science, Data Science, Robotics or related field.
• Experience with Python and machine learning frameworks.
• Interest in representation learning, foundation models, video understanding or CV research.

Responsibilities

• Develop data-efficient computer vision methods for limited annotated data.
• Experiment with self-supervised learning, few-shot learning and foundation models and evaluate results.
• Translate findings into prototypes and research reports and contribute ideas within the team.

Skills

Python

Machine learning

Computer vision

Tools

PyTorch

TensorFlow

Job description

About this position

This internship focuses on data-efficient computer vision, with direct impact on the way we collect data on infrastructure and the built environment. You will work with our team on the challenge of developing computer vision methods that can learn effectively from limited amounts of labelled data, helping to bridge the gap between research prototypes and real‑world applications. Your work may contribute to applications such as infrastructure inspection, asset monitoring, and video-based process monitoring, where collecting large annotated datasets is often impractical. Together with your supervisor, you will define a concrete research question and explore modern approaches such as self-supervised learning, few-shot learning, vision-language models, and foundation models. You will be part of an environment where curiosity is encouraged, ideas are welcomed, and you have the space to develop your expertise while working on applied AI solutions with societal impact.

What will be your role?

There are three options available that we can discuss based on your own interests.

Option 1: Predictive Maintenance from Drone Imagery

You will work on developing computer vision and machine learning methods for automated condition assessment and maintenance prediction from drone-collected imagery. Using modern AI techniques such as foundation models, object detection, image classification, and representation learning, you will investigate how infrastructure defects and asset conditions can be identified reliably from limited annotated inspection data. You will go through different phases of the project, including literature review, dataset exploration, model development, experimentation, and evaluation, and translate your findings into a prototype and research report. Along the way, you will further develop skills in computer vision, machine learning, Python-based AI development, and scientific experimentation, gain knowledge in infrastructure inspection and predictive maintenance, and have the opportunity to contribute your own ideas within our team.

Option 2: Asset Information Extraction from Street-View Imagery

You will develop a data-efficient AI method for automatically extracting asset information from large-scale street-view and aerial imagery. Using computer vision techniques such as object detection, feature extraction, geospatial data integration, and foundation models, you will explore how relevant characteristics of buildings and infrastructure assets can be identified with minimal manual annotation. You will go through different phases of the project, including data analysis, model development, experimentation, and validation, and translate your findings into a prototype and research report. Along the way, you will further develop skills in computer vision, machine learning, geospatial AI, and scientific research, gain knowledge in digital asset management and infrastructure monitoring, and have the opportunity to contribute your own ideas within our team.

Option 3: Video Understanding for Process Monitoring

For this topic, you will work on developing video AI methods for automatic interpretation of processes from video recordings in industrialized construction and related operational environments. Using modern video foundation models, representation learning techniques, and computer vision methods such as action recognition, activity segmentation, pose estimation, and anomaly detection, you will investigate how meaningful process information can be extracted from video data with limited annotation effort. You will go through different phases of the project, including literature review, dataset preparation, model development, experimentation, and evaluation, and translate your findings into a prototype and research report. Along the way, you will further develop skills in video understanding, machine learning, computer vision, and AI research, gain knowledge in process monitoring and workflow analysis, and have the opportunity to contribute your own ideas within our team.

What we expect from you

For this internship, we are looking for a student with a background in Artificial Intelligence, Computer Science, Data Science, Robotics, or a related field, who has an interest in machine learning, computer vision, and AI for real-world applications. You have basic programming skills in Python and some experience with machine learning frameworks such as PyTorch or TensorFlow, or the motivation to quickly develop them.

It helps if you have an interest in topics such as representation learning, foundation models, video understanding, or computer vision research. Experience with reading scientific papers and implementing machine learning methods is beneficial.

Most importantly, you are enthusiastic about exploring new ideas and applying them to practical challenges.

The internship is intended for a period of 6 to 9 months, although the exact duration can be discussed depending on university requirements and the scope of the project. During the internship, you will have plenty of opportunities to learn and grow, including gaining experience with state‑of-the-art machine learning techniques, working with real-world datasets, conducting applied research, and contributing to solutions with societal impact.

Dutch language skills are not required.

What you'll get in return

An internship at TNO means working in an environment where substance and impact are central. You will become part of a knowledge organisation where research and practice come together, and where experts collaborate on solutions to current societal and technological challenges. Your internship is a period in which you can discover what suits you, where your strengths lie and what you would like to learn next. You are part of a professional working environment, gain insight into how things work in practice, and have the opportunity to build experience that goes beyond this internship alone. For many students, an internship is therefore also a first step in discovering whether TNO could be a potential next step after graduation.

In addition, we offer you:

• A professional and innovative internship environment in which you actively contribute to societal and technological challenges, working alongside leading experts in your field.
• Personal and dedicated supervision, with focus on your learning objectives, development and study assignment.
• Room to develop: you gain relevant work experience, develop both your subject‑specific and professional skills, and build a valuable network.
• Use of a laptop and the facilities you need to perform your work effectively.
• A monthly internship allowance of € 615 for a full‑time internship, for MSc, BSc and vocational education (MBO) students.
• Up to eight hours of leave per internship month for a full‑time internship, allowing you to balance your internship with your studies and personal life.
• A contribution towards travel expenses if you are not entitled to a student travel card.
• A free membership to Jong TNO: the network for young colleagues, where you can meet other TNO colleagues and participate in sports activities, professional and personal development activities, and social events such as the annual ski trip.

TNO as an employer

Our people are at the heart of TNO. Their curiosity, expertise and entrepreneurial mindset make it possible to deliver high‑impact research and innovations that contribute to society’s sustainable wellbeing and prosperity. That is why we invest in an inspiring and inclusive working environment where colleagues can excel, have autonomy and continue to grow. Your talent and ambition have every opportunity to flourish at TNO. You work with experts (both within and beyond TNO), have access to advanced technology and the freedom to explore, experiment and innovate. Our strength lies in independence, reliability and collaboration. We find each other in wonder and ingenuity. We are driven to push boundaries. By working with businesses and government, and by connecting different perspectives, we strengthen our innovative capability and create responsible, meaningful results. At TNO, we believe this is our time to help society, government and business move forward faster. Together with driven colleagues, you turn knowledge into concrete innovations or ventures that truly make a difference by combining the power of science and entrepreneurship. And in doing so, you make your mark on our time.

The selection process

TNO will arrange an appropriate internship agreement. If you have any questions about the content of the internship or the application procedure, you can contact the person listed below.

If you start an internship with us, we will also ask you to provide a Certificate of Conduct (VOG).

Important to know before applying

Before the start of the internship, an internship agreement must be signed. For students enrolled at a Dutch university or university of applied sciences, TNO uses the UNL-template, supplemented with several TNO‑specific agreements. For students from foreign educational institutions and MBO (secondary vocational education) programmes, the TNO internship agreement applies. TNO does not sign any other internship agreements.

Before the start of the internship, the educational institution will need to confirm in writing that:you are enrolled at the educational institution for the duration of the internship, and;the internship forms part of your study programme. The confirmation of educational institution takes place by signing the UNL template or forms prepared by TNO.

Interns at TNO must have a registered residential address in the Netherlands at the start of the internship. Carrying out internship activities from abroad is not possible.

Are you an international student? Please note that you must have a BSN (Dutch citizen service number) before the start of your internship. Applying for a BSN can take several weeks, so make sure to start this process well in advance.