Job summary
An opportunity for motivated students to engage in research on uncertainty quantification methods applied to co-simulation technologies in AI-enabled maritime systems. The role involves working with cloud-based platforms to model and analyze the propagation of uncertainties in complex autonomous ship systems, contributing to assurances of situational awareness applications in maritime environments.
Location and work setup
- Location
- Trondheim
- Work setup
- On-site
Responsibilities
Collaborate on the development and application of uncertainty quantification techniques for black-box digital twin models within a co-simulation environment. Develop cloud-based services integrated with simulation platforms to configure, analyze, and visualize uncertainty propagation results. Contribute to research efforts supporting the reliability and trustworthiness of AI-enabled autonomous systems used in maritime scenarios. Engage with real industry data and challenges, enhancing practical skills in simulation and AI research.
Qualifications
Currently enrolled in a Bachelor's or Master's program, preferably in Data and AI/ML, Computer Science, Information Science, Mathematics, Marine Technology, Cybernetics, Robotics, or Physics. Strong interest in AI, machine learning, digital twins, uncertainty quantification, and simulation-based development. Experience in programming with Python and familiarity with AI/ML frameworks such as PyTorch, Scikit-learn, or TensorFlow. Understanding of Bayesian methods and uncertainty quantification principles is beneficial. Knowledge of computer vision, Robot Operating System, sensor fusion, FMI/FMU standards, cloud technologies like Microsoft Azure, and software deployment tools such as CI/CD and Kubernetes is an advantage. Good communication skills in English. Curiosity, initiative, and willingness to collaborate are key. Norwegian language skills are not required.