A total of 28 PhD projects are funded by MAI. In addition, there are 8 associated PhD projects listed below. 

1. Safety, Testing and Assurance of Risk-Aware AI

Candidate: To be recruited
Affiliation: IMT, NTNU / ITK, NTNU
Main supervisor: Ingrid B. Utne
Co-supervisors: Børge Rokseth, Erik Røsæg, Arne Huseby
Industry: DNV (tentative)
Start: 2027
MAI use cases: UC11 (primary), UC4, UC7, possible UC12

Investigates how risk-aware AI agents can support trustworthy, explainable and transparent decision-making, including how machine learning can be connected to risk models and how such systems can be verified and validated.

2. River Ice Breakup Prediction and Mitigation Using Scientific Machine Learning

Candidate: José Antonio Bueno Uceta
Affiliation: IBM, NTNU / MET
Main supervisor: Wenjun Lu
Co-supervisors: Raed Lubbad, Sveinung Løset, Knut Høyland, Knut Tore Alfredsen
Start: 2025
MAI use case: UC12

Develops scientific machine-learning approaches for Arctic ice dynamics, including ML-accelerated collision detection and hybrid physics-informed models for improved prediction of ice runs.

3. Monitoring and Smart Maintenance of Maritime Infrastructure Using Satellite InSAR

Candidate: Hiroyuki Takashina
Affiliation: IBM, NTNU / MET
Main supervisor: Wenjun Lu
Co-supervisors: Raed Lubbad, Sveinung Løset, Konstantinos Christakos
Start: 2025
MAI use case: UC12

Develops physics-informed and data-driven methods for structural health monitoring and anomaly detection in harbour infrastructure using satellite InSAR and other limited data sources.

4A. Organisational Dimensions of Maritime AI

Candidate: Ellen Ingdal
Affiliation: IHB, NTNU / ISS, NTNU
Main supervisor: Marte Fanneløb Giskeødegård
Co-supervisor: Peter Almklov
Industry: Havila / shipping companies
Start: 2026
MAI use cases: UC13, possible UC10

Examines organisational opportunities and barriers to adopting AI in maritime organisations, with particular emphasis on accountability, work practices, competence, organisational structures and relationships between maritime actors.

4B. AI-Enabled Business Model Innovation in the Maritime Industry

Candidate: Svitlana Dobryn
Affiliation: IHB, NTNU / MTP, NTNU
Main supervisor: Viktoriia Koilo
Co-supervisor: Fabio Sgarbossa
Industry: VARD, Ulstein, shipping companies, DNV
Start: 2026
MAI use cases: UC1, UC7

Investigates how value from maritime AI can be quantified across transactions, processes and business models. The project explores concepts such as AI return on investment and the contribution of AI to cost reduction, revenue and operational efficiency.

5. Trustworthy Anomaly Detection for Maritime Systems

Candidate: To be recruited
Affiliation: University of Oslo / possible NTNU collaboration
Main supervisor: Ingrid Glad
Co-supervisors: Martin Tveten, Tor Arne Johansen, Ottar Osen (tentative)
Industry: KNC, Kongsberg Maritime, DNV, Ulstein, Kystverket
Start: 2026
MAI use cases: UC2 (primary), UC4, UC6, UC7

Develops trustworthy machine-learning methods for real-time anomaly detection in streaming and potentially spatio-temporal data, with applications ranging from equipment condition monitoring to maritime traffic surveillance.

6. AI Modelling for Prediction of Waves and Sea Loads on Ships

Candidate: Gerard Grigore Bousquet
Affiliation: IMT, NTNU / MET
Main supervisor: Roger Skjetne
Co-supervisors: Ekaterina Kim, Hans-Martin Heyn
Industry: Kongsberg Maritime
Start: 2026
MAI use cases: UC5, UC10, possible UC3

Develops AI models for phase-resolved wave prediction, sea-load prediction and longer-term prediction of vessel loads, power requirements and energy consumption based on metocean forecasts.

7. Human-Centred Explainable AI for Maritime Decision Support

Candidate: To be recruited
Affiliation: Department of Design, NTNU / AHO
Main supervisor: Elefterios Papachristos
Co-supervisors: Kjetil Nordby, Taufik Sitompul, Ole Andreas Alsos
Start: 2026
MAI use cases: UC4 (primary), UC3, UC5

Explores how AI recommendations, reasoning and uncertainty can be visualised so that seafarers can understand and appropriately act on AI-based decision support, including weather routing and wind-assisted propulsion.

9. Secure and Trustworthy Data Sharing for Maritime AI Development

Candidate: To be recruited
Affiliation: IIR, NTNU / IIK
Main supervisor: Marie Haugli-Sandvik
Co-supervisors: Rune Volden, Ahmed Amro, Erlend Erstad
Start: 2026
MAI use cases: UC2, UC3, UC4

Investigates cybersecurity, organisational and human-centred barriers to sharing maritime operational data for AI development and develops frameworks for secure and trusted data sharing across organisations.

10A. AI-Supported Ship Design

Candidate: To be recruited
Affiliation: IHB, NTNU / IMT
Main supervisor: Henrique Gaspar
Co-supervisor: Stein Ove Erikstad
Industry: VARD
Start: 2026
MAI use case: UC1

Explores AI agents, generative design, knowledge graphs and automation to support industrial ship-design workflows, including layout optimisation, clash detection and verification against rules and regulations.

10B. AI for Accelerated Concept and Tender-Phase Ship Design

Candidate: To be recruited
Affiliation: IHB, NTNU / MTP
Main supervisor: Øystein Bjelland
Co-supervisors: Henrique Gaspar, Erlend Alfnes
Industry: Ulstein, VARD, Breeze Ship Design
Start: 2027
MAI use case: UC1

Develops AI methods for reducing the time required for concept evaluation and tender preparation through rapid estimation, automated data transformation, knowledge graphs and AI-assisted design-space exploration.

11. AI-Based Maritime Traffic Models and Surveillance

Candidate: To be recruited
Affiliation: ITK, NTNU
Main supervisor: Tor Arne Johansen
Co-supervisor: Edmund Brekke
Industry: KNC, FOH, Kystverket
Start: 2026
MAI use cases: UC2 (primary), UC7, UC13

Uses large volumes of AIS and related maritime data to learn vessel trajectories and traffic patterns for anomaly detection, prediction, risk analysis and improved maritime situational awareness.

13. AI-Based Risk and Energy-Efficient Route Planning Using S-100

Candidate: To be recruited
Affiliation: ITK, NTNU / Kartverket / UiO / MET
Main supervisor: Børge Rokseth
Co-supervisor: Ole Andreas Alsos
Industry: Kartverket, MET
Start: 2026
MAI use cases: UC3 and UC5 (primary), UC7, UC10, UC13

Develops risk-based and energy-efficient ship-routing methods using S-100 data products, AI forecasting, vessel-state information and dynamic environmental and traffic data.

14. Human–AI Teaming for Maritime Operations

Candidate: To be recruited
Affiliation: Department of Design, NTNU
Main supervisor: Ole Andreas Alsos
Co-supervisor: Erik Veitch
Industry: DNV
Start: 2026
MAI use cases: UC4 (primary), UC8, UC11

Develops and evaluates interactive concepts for effective human–AI teaming in maritime operations, focusing on human-centred AI, transparency, human factors and interaction design.

15. Agentic AI for Smart Testing of Autonomous Navigation Systems

Candidate: To be recruited
Affiliation: IMT, NTNU / ITK / DNV
Main supervisor: Dong Trong Nguyen
Co-supervisor: Adil Rasheed
Industry: DNV, Kongsberg Maritime
Start: 2026
MAI use case: UC11

Develops multi-agent AI methods for intelligent testing and continuous assurance of autonomous navigation and external situational-awareness systems using simulation, hybrid modelling, formal methods and knowledge management.

16. AI-Supported Condition Monitoring and Predictive Maintenance

Candidate: Sivert Meek Strand
Affiliation: IIR, NTNU / University of Oslo
Main supervisor: Ottar Osen
Co-supervisors: Erlend Coates, Arne Huseby, Vijander Singh (tentative)
Start: 2026
MAI use case: UC6

Develops AI methods for anomaly detection and estimation of remaining useful life, enabling predictive maintenance and improved technical situational awareness for increasingly autonomous vessels.

17. Learning-Based Maritime Radar Tracking and Ship-Trajectory Prediction

Candidate: Martin Quoc-Tuan Buu Huynh
Affiliation: ITK, NTNU
Main supervisor: Edmund Brekke
Co-supervisor: Annette Stahl
Industry: Kongsberg Maritime, DNV, Kystverket
Start: 2026
MAI use cases: UC7 (primary), UC2

Develops learning-based methods for radar detection, target recognition, data association and prediction of vessel intentions and future trajectories in time- and safety-critical maritime applications.

18A. Learning-Based AI Captains for Maritime Training Support

Candidate: To be recruited
Affiliation: IHB, NTNU
Main supervisor: Houxiang Zhang
Co-supervisor: Øivind Kjerstad
Industry: Shipping companies, OSC
Start: 2026
MAI use case: UC8

Develops AI-driven captains that learn from maritime instructors and interact with trainees in advanced simulators, combining LLMs, multimodal interaction, explainable AI and adaptive learning.

18B. Trustworthy Autonomous AI Captains for Simulator Training

Candidate: To be recruited
Affiliation: IHB, NTNU
Main supervisor: Houxiang Zhang
Co-supervisor: Robert Skulstad
Industry: Shipping companies, OSC
Start: 2026
MAI use case: UC8

Develops autonomous AI-controlled vessels for maritime simulators that behave realistically, comply with navigation rules and adapt their manoeuvres to trainee actions.

19. AI-Enhanced Planning and Logistics in Shipbuilding Supply Chains

Candidate: To be recruited
Affiliation: MTP, NTNU / IHB
Main supervisor: Erlend Alfnes
Co-supervisor: Henrique Gaspar (tentative)
Industry: VARD, Ulstein, Brunvoll
Start: 2026

Develops AI-based planning and decision-support methods for shipbuilding supply chains, with an emphasis on resource allocation, disruption prediction and proactive planning of complex engineer-to-order projects.

21. AI-Based Energy Management and Battery Health

Candidate: To be recruited
Affiliation: IMT, NTNU
Main supervisor: Roger Skjetne
Industry: Corvus
Start: 2026
MAI use case: UC10

Develops AI models and agents for cost-optimal energy management and state-of-health monitoring of batteries and fuel cells in ships and integrated port power systems.

22. AI for Underwater Radiated Noise and Propeller Performance

Candidate: To be recruited
Affiliation: University of Oslo / SINTEF / IHB
Main supervisor: Atle Jensen
Co-supervisors: Andreas Austeng, Norwegian Naval Academy
Industry: Kongsberg Maritime
Start: 2026
MAI use case: UC9

Uses AI to identify, classify and quantify underwater radiated noise and cavitation, enabling improved propulsion performance while reducing environmental noise.

23. Certification and Recognition of Safety Levels for Autonomous Navigation

Candidate: To be recruited
Affiliation: University of Oslo
Main supervisor: Henrik Ringbom
Co-supervisors: Erik Røsæg, Sifis Papageorgiou, Øystein Engelhardsen, Arne Huseby
Industry: DNV
Start: 2026
MAI use cases: UC3, UC4, UC6, UC7, UC11

Investigates the legal and regulatory conditions under which autonomous navigation systems can be certified as sufficiently safe and have those certifications recognised across jurisdictions.

24. Maritime Large Language Models and Multimodal AI

Candidate: To be recruited
Affiliation: IDI, NTNU
Main supervisor: Özlem Özgöbek
Co-supervisors: Eric Monteiro, Benjamin Kille
Start: 2027
MAI use cases: UC4 and UC13 (strong links), UC1, UC3, UC6, UC8

Explores maritime applications of large language models and multimodal AI across several of the centre's use cases.

25. Edge AI and Maritime Data Refinement

Candidate: To be recruited
Affiliation: University of Oslo / ITK, NTNU
Main supervisor: Mathias Hudoba de Badyn
Co-supervisor: Andreas Austeng
Start: 2027
MAI use cases: UC1, UC3, UC4, UC6, UC7

Investigates AI functionality at the edge, using digital twins to support data refinement, uncertainty quantification, risk assessment and responsible AI-assisted decision-making onboard ships and across fleets.

26. AI for Ship and Propulsion Design

Candidate: Simon Hauschulz
Affiliation: IMT, NTNU / IHB
Main supervisor: Stein Ove Erikstad
Co-supervisors: Henrique Gaspar, Benjamin Lagemann
Start: September 2026
MAI use case: UC1

Focuses on the application of AI to ship and propulsion design, linked to the centre's AI-supported ship-design activities.

28. AI-Based Multi-Vessel Path Planning for USVs

Candidate: To be recruited
Affiliation: IHB, NTNU / MET
Main supervisor: Øivind Kjerstad
Co-supervisor: Konstantinos Christakos
Industry: Kongsberg and shipping companies
Start: 2026
MAI use case: UC4

Develops collaborative path-planning methods for multiple unmanned surface vessels using nautical charts, AIS, weather, waves and currents. The work addresses collision avoidance, cooperative navigation, energy-efficient routing and dynamic re-routing in adverse conditions.

Associated PhD projects

Advanced Deep Learning for High-Resolution Metocean Data

Candidate: Lenny Jean-Claude Alain Hucher
Affiliation: University of Bergen / MET / TU Delft
Main supervisor: Konstantinos Christakos
Co-supervisors: Øyvind Breivik, George Lavidas
Project: EU-INTERCHANGE
Start: 2025
MAI links: UC3, UC5, UC12

Develops deep-learning methods in combination with numerical modelling to provide high-resolution predictions of waves, currents, temperature, salinity and other metocean conditions.

RS-MAPS PhD – ITK

Candidate: Keira Cant
Affiliation: ITK, NTNU
Main supervisor: Torleiv Bryne
Funding: RS-MAPS / Research Council of Norway
Start: 2026

The project topic is not yet specified in the spreadsheet.

RS-MAPS PhD – IES

Candidate: To be specified
Affiliation: IES, NTNU
Main supervisor: Roger Birkeland
Funding: RS-MAPS / Research Council of Norway
Start: 2026

The project topic is not yet specified in the spreadsheet.

Prediction of Ship Behaviour in Waves

Candidate: To be specified
Affiliation: IHB, NTNU
Main supervisor: Robert Skulstad

Investigates prediction of vessel behaviour under varying wave conditions.

Organisational Implications of Remote Maritime Operations

Candidate: To be specified
Affiliation: IHB, NTNU
Main supervisor: Bjarne Pareliusen

Examines organisational consequences and requirements associated with increasingly remote maritime operations.

Designing for Interactive Team Cognition in Complex Remote Operations

Candidate: To be specified
Affiliation: IHB, NTNU
Main supervisor: Andreas Madsen

Investigates the design of systems and interactions that support interactive team cognition in complex remote operational environments.

Competence and Training for USV Operators

Candidate: To be specified
Affiliation: IHB, NTNU
Main supervisor: Dag Rutledal

Examines competence requirements and training approaches for operators of unmanned surface vessels.

Industrial PhD

Candidate: Zelda Kudzanai Nyangari Molnes
Affiliation: IHB, NTNU
Main supervisor: Marte Fanneløb Giskeødegård
Industry partner: Neuver
Funding: Industrial PhD