Summer Student 2027 - Simulation Technologies

The Research Lab team in Trondheim is looking for 2-3 summer students for the Summer 2027 to join us in applying uncertainty quantification (UQ) methods to co-simulation technologies that support the assurance of AI-enabled systems. 

You will work with the Simulation Trust Center (STC), DNV’s cloud-based co-simulation platform, where users can upload, share, integrate, and simulate black-box digital twin models to generate evidence supporting the External Situation Awareness (ESA) system used in maritime autonomous surface ships.

The EAS system is a key enabler of future autonomous surface ships, providing the situational understanding required for autonomous decision-making. However, ESA systems operate in highly uncertain environments, where uncertainty arises from the wide range of operating conditions encountered at sea, as well as the inherent variability and unpredictability of the AI-enabled components and the surrounding environment. Uncertainties associated with individual components within ESA can propagate through the system and affect overall performance and decision-making. Quantifying and understanding these uncertainties is therefore essential for assessing and assuring the reliability of ESA systems as well as creating trust in the evidence generated by STC.

The summer students will work on estimating uncertainties in different types of black-box models within a co-simulation environment. They will also develop a cloud-based service integrated with STC to configure, analyze, and visualize the results, providing a better understanding of how uncertainties propagate through complex systems.

#summerjob

What we offer:

• An opportunity to work on real industry challenges and real industry data.
• Directly contribute to ongoing research.
• Valuable and real-life experience through our projects
• Meet other young professionals through established networks and informal events
• Opportunity to build a professional network
   

Who are we looking for?

We are looking for curious and motivated students who are interested in AI and machine learning methods, uncertainty quantification, digital twins and simulation-based development.  

Curiosity, initiative and a willingness to collaborate with others to solve technical challenges

Good communication skills in English.

Experience with data processing and AI/ML tools such as PyTorch, Scikit-learn, Tensorflow etc. Understanding in Bayes theorem, Bayesian networks and uncertainty quantification.

Computer vision algorithms, Robot Operating System (ROS), sensor fusion principles, and knowledge in FMI/FMU are an advantage.

Python, Microsoft Azure, CI/CD, Kubernetes. Experience in Clojure or functional programming is a plus (not mandatory).

If you do not meet every qualification listed above, we still encourage you to apply.

We are primarily seeking 3rd and 4th year students with relevant Bachelor/Master Program specialization in:

• Data and AI/ML
• Computer Science / Information Science
• Mathematics
• Marine Technology
• Cybernetics / Robotics
• Physics

Interested? How to apply:

To be considered for this role, please register your CV, application letter, and grade transcripts in our application tracking system (Oracle) electronically. All documents should be in English. If you choose to apply for more than one role in DNV, please state your preference in the application letter.  

Please note: if you either decide to withdraw or want to make changes in your application, you will need to submit a whole new application with a new email address. Please have all documents ready before submitting your final application. 

Application deadline: 16.10.2026. We will evaluate your application after the deadline.

Any questions? Please contact Andreas Hafver at [email protected]

We are looking forward to reviewing your application!

Security and compliance with statutory requirements in the countries in which we operate is essential for DNV. Background checks will be conducted on all final candidates as part of the offer process, in accordance with applicable country-specific laws and practices.