Student Projects

Opportunities for students

The centre offers a wide range of opportunities for master’s and PhD students who want to explore the intersection of AI, engineering, and maritime innovation.

You can take part in:

Master’s projects – Work on real industrial cases in collaboration with partners such as Kongsberg, DNV, VARD, Equinor, and the Norwegian Coastal Administration.
PhD projects and postdoctoral research – Join our network of more than 25 research fellows focusing on AI for the maritime sector. List of open position at JobbNorge.
Student teams and internships – Contribute to ongoing research, prototype development, and innovation activities across Norway’s maritime clusters.

A learning ecosystem that connects research and industry

The centre connects students, researchers, and industry experts through a dynamic and supportive learning environment.

You will gain access to:

The Maritime AI Lab – a world-class sandbox for data, simulation, and testing.
The Shore Control Lab and ship simulators – where students explore human–AI interaction in realistic maritime control settings.
Workshops, hackathons, and competitions, such as NJORD for autonomous vessels.
A vast partner network offering opportunities for part-time work, internships, and thesis collaboration.

Every research activity is supervised by both academic and industrial mentors, ensuring your research makes a real difference.

A pathway to your career

Graduates from MAI-related projects will be well-positioned for careers in:

Maritime technology and AI development
Ship design, simulation, and digital twin solutions
Development of autonomous systems and control centres
Green shipping and sustainability management
Government, regulation, and safety assurance

You’ll join a fast-growing field where digitalisation meets ocean technology, and where your work can directly contribute to safer, cleaner, and smarter seas.

How to get involved

Most student opportunities are hosted through NTNU, University of Oslo, AHO, and Royal Norwegian Naval Academy (RNNA).

Ongoing student projects

Below are ongoing student projects listed (per 07.09.2026).

AI Enabled Prediction of Ship Technical Performance

Student: Bjørn Ferdinand Solbakken Berntzen
Affiliation: IMT, NTNU
Main supervisor: Ekaterina Kim
Co-supervisors: Bingjie Guo and Yi (Edward) Liu, DNV
External stakeholder: DNV
MAI use case: —

Robust Classification of Operational Modes

Student: Theodor Frostad Poll
Affiliation: IMT, NTNU
Main supervisor: Ekaterina Kim
Co-supervisor: Armin Pobitzer, VARD
External stakeholder: VARD
MAI use case: —

MPC- and AI-based Energy and Emission Management for Ships with Batteries

Students:

Irene Sævik — IMT, NTNU
Eline Sofie Hestsveen — IMT, NTNU

Main supervisor: Roger Skjetne
Co-supervisor: TBD, possibly SINTEF Nordvest
External stakeholders: Corvus, Havila Kystruten
MAI use case: UC10

Teaching AI the Language of Ship Brokers

Students:

Tora Johanne Fredheim — ITK, NTNU
Lule Haugsbø Øyberg — ITK, NTNU
Åsmund Løvoll — IDI, NTNU

Main supervisors: Tor Arne Johansen (ITK); Benjamin Kille (IDI)
External stakeholder: Astrup-Fearnley
MAI use case: UC13

Ghost Ship Tracking Using Satellite Sensors, Machine Learning and Sensor Fusion

Student: Vegard Mitsem Heggland
Affiliation: ITK, NTNU
Main supervisor: Tor Arne Johansen
External stakeholders: Kystverket, FOH
MAI use case: UC2

Predicting Port Congestion for Chemical Tankers Using Machine Learning

Students:

Alexander Edvard Aschehoug — ITK, NTNU
Christian Naustvik Økland — ITK, NTNU
August Hopstad Nykvist — IMT, NTNU
Matiss Podins — IDI, NTNU

Main supervisors: Tor Arne Johansen (ITK); Ekaterina Kim (IMT); Benjamin Kille (IDI)
Co-supervisor: Thordis Thorarinsdottir
External stakeholders: Astrup-Fearnley, Utkilen
MAI use case: UC13

Machine Learning-based Prediction of Ship Trajectories for Collision Avoidance

Students:

Arthur Leiv Claude Prévault Aabakken — ITK, NTNU
Synne Marie Alstergren — ITK, NTNU
Erik Alexander Standal — ITK, NTNU

Main supervisor: Tor Arne Johansen
External stakeholder: Kongsberg Maritime
MAI use cases: UC2, UC7

Dark Ship Tracking Using Satellite Sensors, Machine Learning and Sensor Fusion

Students:

Jesper Nilsson Lybeck — ITK, NTNU
Julian Martens Meyer — ITK, NTNU

Main supervisor: Tor Arne Johansen
External stakeholders: Kystverket, FOH, Norcontrol, VAKE
MAI use case: UC2

Modular Structured State Space Architecture for Nonlinear Maritime Market and Operational Field Modeling

Student: Atle Sund
Affiliation: ITK, NTNU
Main supervisor: Tor Arne Johansen
Co-supervisor: Thordis Thorarinsdottir
External stakeholders: Astrup-Fearnley, Utkilen
MAI use case: UC13

Image-based Satellite Tracking of Dark Ships Using In-orbit AI

Student: Maya Erica Frafjord Saint-Victor
Affiliation: ITK, NTNU
Main supervisor: Tor Arne Johansen
Co-supervisor: Torstein Solberg, VAKE
External stakeholder: VAKE
MAI use case: UC2

Multi-hypothesis Tracking for Maritime Surveillance

Student: Bjørn Magnus Sætrom
Affiliation: ITK, NTNU
Main supervisor: Edmund Brekke
Co-supervisors: Tor Arne Johansen; Brita Gade, FFI
External stakeholder: FFI
MAI use case: UC2

Data Collection and AI Model Exploration for Predictive Power Source Protection

Student: William Haugland
Affiliation: IIR, NTNU
Main supervisor: Ottar L. Osen
Co-supervisors: Erlend M. Coates; Henning Sandbakk, Vard Electro AS
External stakeholder: VARD
MAI use case: UC6

A Condition-Monitoring Dashboard with Uncertainty-Aware Predictive Maintenance for Marine Vessels and Equipment

Student: Kenneth A. Bekkeli
Affiliation: IIR, NTNU
Main supervisor: Agus I. Hasan
Co-supervisor: Stig Espeseth, Seaonics AS
External stakeholder: Seaonics AS
MAI use case: UC6

Autonomous Maritime Mothership: Coordinated UAV/ROV Deployment and GNSS-Free Navigation

Students:

Ansgar Korsæth — MTP, NTNU
Mikkel Bølset Gisleberg — MTP, NTNU
Nadine Issa — MTP, NTNU
Stian Sandanger — MTP, NTNU

Main supervisor: Martin Steinert
Co-supervisors: Kim Christensen, Øystein Bjelland
External stakeholder: Maritime Robotics
MAI classification: Tier-3

Student projects - MAI