Summer Student
Posted 15 Sep 2026
Advertised as “Summer Student 2027 - Risk, Simulation and Digital Evidence”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Contract
- Internship
No salary listed for this role.
Field of studyApplied Physics, Computer Science, Data Science, Engineering Cybernetics, Engineering Physics, Marine Technology, Mathematics, Nuclear Engineering
Study levelBachelor's
We are looking for students in their 3rd or 4th year of studies who are interested in simulation, complex systems, uncertainty and the future of safety-critical technology.
Modern engineering systems are increasingly supported by simulations, digital twins, AI models and large volumes of operational data. A fundamental challenge is understanding when these models can be trusted and how their results can be used to support important engineering and safety decisions.
In this project, you will explore how simulations, risk assessments and operational data can be used to build confidence in complex engineering systems.
Working together with students focusing on assurance and system understanding, you will investigate questions such as:
- How can we model and understand uncertainty in complex systems?
- How can simulations be used to support engineering decisions?
- What makes a digital twin trustworthy?
- How can evidence be generated, validated and communicated?
- How can we determine whether a model is sufficiently accurate for its intended purpose?
- How can future classification and certification processes make use of simulation-based evidence?
The work combines elements of simulation, risk analysis, uncertainty quantification, AI-enabled engineering and decision support for complex technical systems.
#summerjob
Responsibilities
Working together with students, focusing on simulation and risk analysis, you will investigate questions such as:
- How can we understand the impact of design changes, software updates or maintenance actions?
- How can requirements be traced throughout the lifecycle of a system?
- What information is needed to justify confidence in a safety-critical system?
- How can engineering evidence be structured and updated as systems evolve?
- How can future classification and certification processes become more digital, dynamic and data-driven?
The work combines elements of systems thinking, software engineering, safety, AI-enabled engineering and decision support for complex technical systems.
DNV is an Equal Opportunity Employer and gives consideration for employment to qualified applicants without regard to gender, religion, race, national or ethnic origin, cultural background, social group, disability, sexual orientation, gender identity, marital status, age or political opinion. Diversity is fundamental to our culture and we invite you to be part of this diversity.
Qualifications
We are looking for students with interest in:
- Engineering Physics
- Applied Physics and Mathematics