Master’s degree1,500 h · Online · 11 modules

Master's in Artificial Intelligence for Maritime Transport

This master's offers an in-depth look at how the latest AI technologies can optimize and transform the maritime industry. You will learn to develop and deploy intelligent solutions for fleet management, route optimization, predictive maintenance and onboard safety, improving efficiency and reducing costs, and gain the skills to lead the digital transformation of shipping with machine learning, computer vision and big data analytics.

Duration1,500 h

ModeOnline

Start1 December

Modules11

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About the program

What is the Master's in Artificial Intelligence for Maritime Transport?

This master's covers the full chain of artificial intelligence in shipping, from the planning office to the bridge. You will study mathematical optimization and AI for route planning and maritime logistics, with linear, integer and stochastic programming, genetic algorithms, reinforcement learning and demand forecasting with ARIMA and recurrent networks; intelligent and autonomous navigation systems, multisensor perception with radar, LIDAR, sonar, AIS and cameras, sensor fusion with Kalman and particle filters and COLREG-compliant decision-making; deep neural networks such as CNN, RNN, LSTM and Transformers for control, perception and predictive management, deployed with TensorFlow, PyTorch and ONNX on onboard hardware; digital twins and distributed AI for autonomous fleets; cybersecurity for smart ships, from SCADA protection to AI-based intrusion detection; and the ethics, regulation and governance of AI at sea, including the IMO MASS Code and the European AI Act.

What sets this master's apart: it treats AI as a tool for the whole transport operation, connecting route and fleet optimization with autonomy, predictive maintenance and cyber resilience instead of focusing on a single application; it goes down to implementation, with model training, hyperparameter tuning, deployment on embedded hardware and robustness against adversarial attacks; it dedicates a full module to ethics, liability, explainability and the certification of AI systems by classification societies; and it closes with a Final Project in which you integrate autonomous navigation, cybersecurity and digital twins into a complete management system for an autonomous fleet.

Program typeMaster’s degree

ModeOnline

Duration1,500 hours

Modules11

Start1 December

Program highlights

AI-based route and logistics optimization: mathematical programming, genetic algorithms, reinforcement learning and demand forecasting.
Autonomous navigation: multisensor perception, sensor fusion, collision avoidance and COLREG rules embedded in AI.
Deep learning for ships: CNN, RNN, LSTM and Transformers deployed with TensorFlow, PyTorch and ONNX.
Digital twins and distributed AI for predictive management of autonomous fleets.
Cybersecurity of smart ships and AI governance: IMO MASS Code, EU AI Act, liability and certification.
Final Project: an integrated autonomous fleet management system with cybersecurity and digital twins.

Syllabus

Syllabus of the Master's in Artificial Intelligence for Maritime Transport

11 modules. Select a module to see its content.

Module 1

Advanced route and maritime logistics optimization with artificial intelligence algorithms and predictive analysis

1.1. Foundations of optimization in maritime transport: problem definition, key variables and multidimensional objectives
1.2. Advanced mathematical models for route planning: linear, nonlinear, integer and stochastic programming
1.3. Integration of artificial intelligence algorithms: supervised, unsupervised and reinforcement learning applied to naval logistics
1.4. Predictive analysis of demand and capacity: time series, recurrent neural networks (RNN) and ARIMA models to anticipate changes in maritime traffic
1.5. Genetic algorithms and evolutionary optimization: design and application for optimal route selection and allocation of naval resources
1.6. Deep neural networks applied to pattern detection in navigation data and real-time sea conditions
1.7. Simulation and modeling of operational scenarios under uncertainty: Monte Carlo techniques and agent-based simulation for risk assessment
1.8. Optimization of energy consumption and emissions reduction through adaptive algorithms in logistics planning
1.9. Big data and advanced analytics applied to maritime logistics: integration and processing of heterogeneous sensor, AIS, weather and port traffic data
1.10. Development of AI-based decision support systems (DSS) for continuous improvement of fleet and route management

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 2

Intelligent autonomous navigation systems and real-time decision-making for commercial ships

2.1. Foundations of intelligent systems: artificial intelligence, machine learning and deep learning applied to ship navigation
2.2. Architecture of autonomous systems for ships: sensors, actuators, control platforms and inter-system communications
2.3. Multisensor fusion and real-time processing: integration of radar, LIDAR, AIS, camera and inertial system data
2.4. Predictive modeling and simulation of maritime behavior: trajectory prediction, obstacle detection and risk assessment in dynamic environments
2.5. Real-time decision-making algorithms: optimal route planning, collision avoidance and adaptive management of adverse weather
2.6. Neural networks and deep techniques for interpreting signals and patterns for safe autonomous navigation
2.7. Autonomous control and navigation systems: advanced PID controllers, adaptive control and fuzzy inference for precise maneuvers
2.8. Implementation of knowledge systems and COLREG rules integrated into artificial intelligence for automatic regulatory compliance
2.9. Communication networks and secure protocols for data transfer between autonomous platforms and remote control centers
2.10. Continuous monitoring and supervision: early warning systems, predictive diagnostics and preventive maintenance of autonomous systems

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 3

Optimization techniques and predictive analysis for autonomous maritime routes and logistics

3.1. Foundations of mathematical optimization: linear, nonlinear and integer programming applied to maritime logistics
3.2. Stochastic models for predictive analysis: Markov chains, Poisson processes and Monte Carlo simulation on shipping routes
3.3. Advanced machine learning techniques: deep neural networks, reinforcement learning and genetic algorithms for predicting and optimizing autonomous routes
3.4. Real-time data integration: maritime IoT sensors, AIS, weather and oceanography for predictive systems
3.5. Multi-objective route planning algorithms: minimizing costs, times, emissions and risks in autonomous transport
3.6. Optimization under dynamic constraints: modeling and solving problems with variable limitations such as sea state, traffic and regulations
3.7. Simulation and validation of scenarios: digital twins to test autonomous navigation strategies and integrated maritime logistics
3.8. Big data and data mining in fleet management and anticipation of logistics demand
3.9. AI-based decision support systems: dashboards, predictive alerts and automated recommendations for operators and managers
3.10. Applied cases and integrative projects: complete development of an optimization and predictive analysis system for autonomous fleets on commercial routes

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 4

AI architectures, perception and multisensor fusion for autonomous control, digital twins and cybersecurity in smart ships

4.1. Foundations of AI architectures in naval systems: neural models, deep learning and convolutional networks applied to maritime transport
4.2. Advanced multisensor perception: integration and processing of radar, sonar, optical camera and LIDAR signals to recognize and classify objects in complex marine environments
4.3. Sensor fusion techniques: probabilistic fusion, Kalman filters, particle filters and multisensor decision methods to increase the reliability and robustness of autonomous navigation
4.4. Autonomous control of smart ships: design and implementation of adaptive controllers based on reinforcement learning and real-time trajectory planning
4.5. Development and application of maritime digital twins: dynamic ship modeling, real-time simulation, predictive monitoring and AI-based optimization of operations
4.6. Cybersecurity strategies for naval AI systems: vulnerability analysis, AI-based intrusion detection, resilience to cyberattacks and secure communication protocols for autonomous systems
4.7. Integration of AI and automation systems: distributed architectures, edge computing and cloud systems for data and process management on smart ships
4.8. International regulations and standards for implementing AI in maritime transport: regulatory compliance, validation and certification of autonomous systems
4.9. Case studies and real applications: innovation projects in digital twins, multisensor perception and autonomous control at leading shipping companies
4.10. Development of advanced prototypes and simulators: tools for testing and validating AI algorithms in simulated environments that replicate real sea conditions

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 5

Design and implementation of deep neural networks for autonomous control and predictive management in smart ships

5.1. Foundations of deep neural networks: multilayer perceptrons, activation functions, backpropagation and optimization
5.2. Advanced architectures for maritime systems: CNN, RNN, LSTM and Transformers adapted to maritime transport
5.3. Model design for autonomous control systems: integration of neural networks into model predictive control (MPC) for smart ships
5.4. Supervised and unsupervised learning on marine data: preprocessing of sensor signals, anomaly detection and clustering of operational behavior
5.5. Implementation of networks for predictive management: forecasting sea conditions, predictive maintenance of machinery and energy optimization
5.6. Development and integration of intelligent perception systems: deep learning-based sensor fusion for safe navigation and autonomous handling in dynamic environments
5.7. Training and hyperparameter tuning techniques: regularization, dropout and data augmentation techniques specific to maritime data
5.8. Platforms and tools for real-time deployment: TensorFlow, PyTorch, ONNX and deployment on onboard hardware
5.9. Model evaluation and validation: performance metrics, tests in maritime simulators and real test scenarios
5.10. Safety and robustness considerations: mitigation of adversarial attacks, fault tolerance and reliability in autonomous naval systems

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 6

Autonomous control, perception and cybersecurity systems for smart ships with deep neural networks and predictive analysis

6.1. Foundations and architecture of autonomous control systems on ships: dynamic modeling, embedded systems and maritime communication protocols
6.2. Deep neural networks for maritime perception: design, training and optimization of CNN and RNN applied to optical sensors and radar
6.3. Advanced sensor fusion: integration of LIDAR, multibeam sonar and hyperspectral camera data for navigation and obstacle detection in complex maritime environments
6.4. Predictive analysis algorithms for predictive maintenance and operational optimization: statistical models and machine learning to anticipate failures in critical naval systems
6.5. Implementation of advanced assistance systems adapted to smart ships for autonomous and semi-autonomous maneuvers with built-in safety protocols
6.6. Maritime cybersecurity architecture and protocols: defense against cyberattacks, integrity of SCADA systems and access management on ship networks
6.7. Detection and mitigation of cyber threats with machine learning: real-time network traffic analysis and adaptive response to intrusions
6.8. International regulations and standards on autonomy and cybersecurity in maritime transport: IMO, IEC 62676 and IEC 61162-460
6.9. Integration of autonomous systems with cloud-based control platforms and edge computing for fleet operations and remote supervision
6.10. Case studies and autonomous navigation simulations: neural networks for route optimization, response to adverse weather and collaborative management of maritime traffic

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 7

Advanced optimization, autonomous control and cybersecurity strategies for smart ships with neural networks and predictive analysis

7.1. Foundations of advanced optimization for naval systems: classical techniques, metaheuristics and evolutionary algorithms applied to the operational efficiency of smart ships
7.2. Design and implementation of autonomous control on ships: system architecture, integrated sensors and smart actuators for precise and safe maneuvers
7.3. Deep neural networks applied to predicting sea conditions and system performance: supervised and unsupervised models, training, validation and deployment in real environments
7.4. Predictive maintenance analysis based on onboard sensor data: early fault detection, prognostics and planning of interventions to reduce costs and downtime
7.5. Adaptive and robust control algorithms for autonomous navigation: response to dynamic disturbances and adverse environmental conditions
7.6. Integration of maritime cyber-physical systems: secure real-time communication between sensors, control platforms and command centers
7.7. Cybersecurity protocols and standards in maritime transport: threat assessment, vulnerability management and risk mitigation on smart ships
7.8. Machine learning techniques for detecting and responding to cyberattacks: early warning and self-defense systems in networks of connected ships
7.9. Advanced simulation and digital twins for route and operations optimization in autonomous fleets: scenario modeling and analysis of impact on performance and safety
7.10. International regulations and best practices for adopting artificial intelligence in the naval sector: legal compliance, ethics and technology governance

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 8

Integration of distributed AI systems and digital twins for predictive management and optimization of autonomous fleets

8.1. Foundations of distributed AI: distributed architectures, communication models, interoperability protocols and security in multi-agent systems for the naval domain
8.2. Design and development of digital twins for vessels: dynamic modeling, calibration with real-time sensor data and predictive simulation of structural and operational behavior
8.3. Integration of heterogeneous data: fusion of IoT sensor, radar, SCADA and AIS information to continuously feed predictive models
8.4. Machine learning and reinforcement learning algorithms for optimizing routes, energy consumption and intelligent resource allocation in autonomous fleets
8.5. Definition and application of performance and resilience metrics in distributed maritime AI systems: key indicators for predictive maintenance and automated decision-making
8.6. Advanced protocols for secure communication and synchronization between onboard AI nodes and shore platforms: blockchain, cryptography and digital identity management
8.7. Implementation of adaptive control systems based on digital twins for real-time fault management, early warnings and mitigation of operational risks
8.8. Case studies: practical application in autonomous fleet simulators, integration with port management systems and reduction of environmental footprint through AI
8.9. Design of scalable and redundant architectures for deploying distributed AI in highly complex maritime environments and adverse conditions
8.10. Advanced analysis of results and continuous optimization through feedback loops between digital twins and AI systems

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 9

Design and implementation of autonomous navigation, multisensor perception and cybersecurity systems for smart ships with deep neural networks and digital twins

9.1. Advanced foundations of autonomous navigation system design for smart ships: modular architecture, redundancy and fault tolerance
9.2. Integrated multisensor perception: fusion of LIDAR, radar, hyperspectral camera, sonar and GNSS data for real-time 3D mapping
9.3. Modeling and processing of sensor signals with deep neural networks: CNN, RNN and hybrid architectures for detecting and classifying objects in complex maritime environments
9.4. Practical implementation of digital twins in maritime transport: predictive simulation, real-time monitoring and operational optimization with reinforcement learning
9.5. Adaptive autonomous control algorithms: optimal route planning, collision avoidance and decision-making under uncertainty with probabilistic models and deep reinforcement learning
9.6. Integration of cyber-physical systems for smart ship security: secure communication protocols, intrusion detection and automatic AI-based response to cyberattacks
9.7. Cryptography applied to maritime communication networks: blockchain and advanced encryption techniques to guarantee the integrity and confidentiality of critical data
9.8. International regulations and technical standards for autonomous navigation and naval cybersecurity: SOLAS, IMO, IEC 62998 and IMO recommendations on autonomous systems
9.9. Development and integration of intelligent human-machine interfaces: augmented reality for remote supervision and assisted control in navigation and maneuvering
9.10. Case studies and practical projects: design and simulation of complete autonomous navigation and cyber resilience systems for state-of-the-art naval and commercial platforms

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 10

Ethics, regulation and governance of AI in maritime transport

10.1. The IMO regulatory framework for autonomous ships (MASS Code)
10.2. The European Artificial Intelligence Act
10.3. Legal liability of autonomous systems
10.4. Ethics of algorithmic decision-making
10.5. Explainability and transparency of models
10.6. Quality and bias in maritime data
10.7. Certification of AI systems by classification societies
10.8. Human factors and remote supervision
10.9. Data protection and privacy
10.10. Strategies for the responsible adoption of AI

At this stage

136 hestimated workload

11 wkweeks at your pace

Module 11

Final Project

11.1. Comprehensive architecture of autonomous systems on ships: sensors, actuators, control systems and communication networks in the maritime environment
11.2. Advanced autonomous navigation models: route planning, obstacle avoidance and real-time decision-making in dynamic sea conditions
11.3. Digital twins applied to naval management: conceptualization, design, simulation and validation to replicate the operational and structural behavior of autonomous fleets
11.4. Integration of artificial intelligence for multidimensional optimization: deep learning, neural networks and optimization algorithms for energy consumption, predictive maintenance and operational efficiency
11.5. Maritime communication platforms for autonomous fleets: protocols, latency, redundancy and security in data transmission between vessels and control centers
11.6. Cybersecurity in autonomous maritime transport systems: risk assessment, specific attacks such as spoofing, malware and intrusion, and AI-based defense strategies
11.7. International regulations and technical standards for the safe operation of autonomous fleets: compliance with IMO and SOLAS and cybersecurity recommendations
11.8. Implementation of real-time monitoring systems: big data analysis, anomaly detection and alert generation for tactical and strategic decision-making
11.9. Architecture and management of the centralized control platform for autonomous fleets: hardware-software integration, redundancy, scalability and interoperability protocols
11.10. Development of advanced simulation scenarios with digital twins: evaluation of navigation strategies, emergency response and resource optimization in autonomous maritime operations

At this stage

136 hestimated workload

11 wkweeks at your pace

Plan your pace

Duration and format of the Master's in Artificial Intelligence for Maritime Transport

It is 1,500 hours. Tell us how many you can study each week and we will tell you when you will really finish.

Hours per week

4 h30 h

With 12 h per week

months to finish

125 weeks

weeks in total

This is the pace at which most students finish. It fits well with the installment plan.

Faculty

Academic leadership and faculty

The Navalis teaching team: working professionals who teach and supervise the programs.

Dr. Marina AlcoverAcademic Director and Head of Quality Assurance

Eng. Diego CifuentesSenior Lecturer in Naval Welding and Non-Destructive Testing; Head of QA/QC for Welded Joints

Capt. Javier Abaroa (MCA)Senior Lecturer in Ship Handling, Electronic Navigation (ECDIS/Radar/ARPA) and GMDSS; STCW Compliance and Bridge Resource Management Coordinator

Dr. Pau FerrerSenior Lecturer in Naval Architecture and Simulation; Director of the Hydrodynamics and CFD Laboratory

Dr. Nuria LlobregatSenior Lecturer in Marine Meteorology, SAR and Operational Safety; Navigation Risk Management Coordinator

Eng. Javier BañulsSenior Lecturer in Electrical Engineering, Automation and ETO Engineering; Electronic Integration and Engine Alarms Coordinator

Eng. Sofía MarquinaSenior Lecturer in Materials, Composites and Corrosion Protection; Coordinator of the Advanced Paints and Coatings Line

Eng. Tomás RieraSenior Lecturer in Propulsion, Alternative Energy and Hybrid Systems; Energy Performance and Vibration Coordinator

Objectives

Objectives of the Master's in Artificial Intelligence for Maritime Transport

01Optimize fleet management by implementing fleet management systems that monitor position, fuel consumption and maintenance, optimizing routes and reducing operating costs.

02Develop autonomous navigation systems that integrate advanced environmental perception with LiDAR, cameras and radar and route planning algorithms that are robust to uncertainty and energy-efficient.

03Implement predictive models for vessel maintenance, analyzing sensor data and maintenance records to predict failures, optimize scheduling and reduce costs.

04Improve energy efficiency in maritime transport by optimizing routes and speeds with real-time weather and ocean data to minimize resistance and fuel consumption.

05Predict and mitigate operational risks in real time with predictive analysis based on historical data and simulations, identifying vulnerabilities and triggering contingency plans when critical deviations are detected.

06Design and implement intelligent cargo and voyage management systems that take sea state, currents and weather into account to minimize fuel consumption and environmental impact.

Careers

Career opportunities after the Master's in Artificial Intelligence for Maritime Transport

01Developer of ship route optimization algorithmsimproving efficiency, reducing fuel consumption and minimizing environmental impact.

02Autonomous navigation systems specialistdesign, implementation and testing of intelligent control and navigation systems for ships.

03Real-time data analyst for maritime traffic managementcongestion prediction, safety optimization and port efficiency.

04AI consultant or software engineer for the naval industryadoption of AI solutions and development of software for ship control systems.

05Cybersecurity specialist for intelligent navigation systemsprotection of autonomous navigation systems against cyberattacks and vulnerabilities.

06AI researcher or innovation project manager in the naval sectornew machine learning techniques and R&D projects for maritime applications.

Frequently asked questions

Questions about the Master's in Artificial Intelligence for Maritime Transport

Maritime transport: shipping companies, fleets, ports and the ships themselves, covering route and logistics optimization, autonomous navigation, predictive maintenance, cybersecurity and regulation.

Autonomous navigation is one part of the program. The master's also covers route and fleet optimization, demand forecasting, predictive management, distributed AI for fleets, cybersecurity and the ethics and regulation of AI in shipping.

Basic knowledge of Python or R, statistics, linear algebra and calculus is recommended. Familiarity with TensorFlow or PyTorch is useful, and the program develops these skills through practical projects.

Yes. Merchant marine officers and captains can use it to understand and lead the adoption of intelligent systems for navigation and safety, alongside naval engineers, port and logistics professionals, developers and data scientists.

Yes. A full module covers the IMO MASS Code, the European AI Act, liability of autonomous systems, explainability, data bias, certification by classification societies and remote supervision.

Online, with a flexible methodology, practical projects with maritime data and contact with experts in the field, designed for working professionals.

Through practical projects in each module and a Final Project in which you integrate autonomous navigation, cybersecurity and digital twins into a management system for an autonomous fleet.

A Navalis Institute master's certificate and a portfolio of AI projects, including your Final Project, that you can show to employers.

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Online admission

Admission process

No waiting for a call back. If your documents are in order, you can enroll in the same session.

Entry requirements

Profile: a degree in computer engineering, mathematics, physics, telecommunications engineering or a related field, or proven professional experience in artificial intelligence or maritime transport; basic knowledge of Python or R, statistics, linear algebra and calculus is recommended.
Language: sufficient command of English to follow the classes and the technical documentation.
Technical resources: a computer capable of running data analysis software and AI models and a high-speed internet connection.
Admission: online application, review of your profile and a short interview with the academic team.

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