Summer Student 2027 - Simulation Technologies

DNV Germany Holding GmbH

Trondheim

On-site

NOK 201,000 - 312,000

Full time

2 days ago
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Job summary

DNV The Research Lab team in Trondheim is looking for 2-3 summer students for 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 ESA system used in maritime autonomous surface

Qualifications

  • Summer students will work on estimating uncertainties in different types of black-box models within a co-simulation environment.
  • They will develop a cloud-based service integrated with STC to configure, analyze, and visualize the results.

Responsibilities

  • Apply uncertainty quantification methods to co-simulation environments.
  • Develop a cloud-based service integrated with STC to configure, analyze, and visualize uncertainty propagation across the system.

Job description

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.

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