Dr. Marina AlcoverAcademic director focused on measurable results and operational excellence in maritime education. Integrates IMO/STCW regulations and ISO 21001/9001 f
Masters · 1500 h · 11 modules
Master's in Artificial Intelligence applied to Naval Transport
Offers an in-depth immersion in the application of the latest AI technologies to optimize and revolutionize the maritime industry. You will learn to develop and implement intelligent solutions for fleet management, route…
Duration1500 h
FormatOnline
Starts28 December
Places20 / 20
Prior knowledge
Prior Knowledge Assessment
These are the skills this programme builds. Tick the ones you already have and we will tell you which part of the syllabus is really for you.
Your profile
Tick what you already know to see your result.
Programme overview
What is the Master’s in Artificial Intelligence applied to Naval Transport?
The Master's in Artificial Intelligence Applied to Naval Transport
Offers an in-depth immersion in the application of the latest AI technologies to optimize and revolutionize the maritime industry. You will learn to develop and implement intelligent solutions for fleet management, route optimization, predictive maintenance, and onboard safety, boosting efficiency and reducing costs. This program provides you with the skills necessary to lead the digital transformation of the maritime sector, addressing real-world challenges with machine learning algorithms, computer vision, and big data analytics.
Key Advantages
- Practical Approach: Development of real-world projects using data from the maritime sector.
- Industry Experts: Faculty with experience in AI and maritime transport.
- Cutting-Edge Tools: Access to industry-leading AI software and platforms.
- Professional Networking: Connection with companies and experts in the maritime and technological fields.
- Adaptability: Flexible format to combine studies with your career professional.
Programme type
Master's Degree
Study mode
Online
Duration
1,500 hours
Modules
11
Programme highlights
- Master AI: Learn to apply the latest Artificial Intelligence techniques in the Maritime Transport sector.
- Optimization and Efficiency: Improve the safety, efficiency, and sustainability of maritime operations with intelligent solutions.
- Predictive Analytics: Anticipate failures, optimize routes, and manage resources with predictive models based on naval data.
- Real-World Case Studies: Work with real-world data and solve specific industry challenges, from fleet management to port automation.
- Expert Professionals: Learn from a team of AI and maritime transport experts with experience in cutting-edge projects.
- Boost your career and lead the digital transformation of maritime transport with this specialized master's degree.
Curriculum
Programme Structure
The programme is structured in 11 modules, listed below in order of study.
01
Advanced route optimisation and maritime logistics through artificial intelligence algorithms and predictive analysis
- Fundamentals of optimisation in maritime transport: definition of problems, key variables and multidimensional objectives
- Advanced mathematical models for route planning: linear, non-linear, integer and stochastic programming
- Integration of artificial intelligence algorithms: supervised, unsupervised learning and reinforcement applied to naval logistics
- Predictive analysis of demand and capacity: use of time series, recurrent neural networks (RNN) and ARIMA models to anticipate variations in maritime traffic
- Genetic algorithms and evolutionary optimisation: design and application for optimal route selection and naval resource allocation
- Deep neural networks applied to pattern detection in real-time navigation data and maritime conditions
- Simulation and modelling of operational scenarios under uncertainty: Monte Carlo techniques and agent-based simulation for risk assessment
- Optimisation of energy consumption and emission reduction through adaptive algorithms in logistics planning
- Big data and advanced analytics applied to maritime logistics: integration and processing of heterogeneous data from sensors, AIS, climate and port traffic
- Development of AI-based decision support systems (DSS) for continuous improvement of fleet and route management
02
Intelligent autonomous navigation systems and real-time decision making for commercial ships
- Fundamentals of intelligent systems: artificial intelligence, machine learning and deep learning applied to naval navigation
- Autonomous systems architecture for ships: sensors, actuators, control platforms and inter-system communications
- Multisensory fusion and real-time processing: integration of data from radar, lidar, AIS, cameras and inertial systems
- Predictive modelling and simulation of maritime behaviours: trajectory prediction, obstacle detection and risk assessment in dynamic environments
- Real-time decision making algorithms: optimal route planning, collision evasion and adaptive management in the face of adverse weather conditions
- Neural networks and deep techniques applied to the interpretation of signals and patterns for safe autonomous navigation
- Autonomous control and navigation systems: advanced PID controllers, adaptive control and diffuse inference for precise manoeuvres
- Implementation of knowledge systems and rules of Colreg integrated in artificial intelligence for automatic regulatory compliance
- Secure communication networks and protocols for data transfer between autonomous platforms and remote control centres
- Continuous monitoring and monitoring: early warning systems, predictive diagnostics and preventive maintenance of autonomous systems
03
Optimisation techniques and predictive analysis for autonomous maritime routes and logistics
- Fundamentals of Mathematical Optimisation: Linear, non-linear and integer programming applied to maritime logistics
- Stochastic models for predictive analysis: Markov chains, Poisson processes and Monte Carlo simulation on naval routes
- Advanced machine learning techniques: deep neural networks, reinforced learning and genetic algorithms for predicting and optimising autonomous routes
- Real-time data integration: IoT maritime, AIS, meteorology, and oceanography for predictive systems
- Multi-objective route planning algorithms: minimisation of costs, times, emissions and risks in autonomous transport
- Optimisation under dynamic constraints - modelling and troubleshooting with variable limitations such as sea state, traffic and regulations
- Scenario simulation and validation: Use of digital twins to test autonomous navigation and integrated maritime logistics strategies
- Application of big data and data mining in the management of naval fleets and anticipation of logistics demands
- AI-based decision support systems: dashboard, predictive alerts and automated recommendations for operators and managers
- Applied cases and integrative projects: Complete development of an optimisation and predictive analysis system for autonomous fleets in commercial routes
04
AI, perception and fusion architectures for autonomous control, digital twins and cybersecurity on smart ships
- Fundamentals of AI architectures in naval systems: neural models, deep learning and convolutional networks applied to maritime transport
- Advanced multi-sensory perception: integration and processing of signals from radar, sonar, optical cameras and LiDAR for object recognition and classification in complex marine environments
- Sensory Fusion Techniques: Probabilistic Fusion Algorithms, Kalman Filter, Particle Filters and Multisensory Decision Making Methods to Increase Reliability and Robustness in Autonomous Navigation Systems
- Autonomous control of intelligent ships: design and implementation of adaptive controllers based on real-time reinforcement and trajectory planning for safe and efficient operations
- Development and application of maritime digital twins: dynamic ship modelling, real-time simulations, predictive monitoring and optimisation of operations using AI techniques
- Cybersecurity strategies in Naval AI systems: vulnerability analysis, intrusion detection based on artificial intelligence, resilience to cyber attacks and secure protocols for the communication of autonomous systems
- Integration of AI and automation systems: distributed architectures, edge computing and cloud systems for data and process management in smart ships
- International regulations and standards for the implementation of AI in naval transport: regulatory compliance, validation and certification of autonomous systems
- Real Case Studies and Applications: Analysis of Innovation Projects in Digital Twins, Multisensory Perception and Autonomous Control in Leadership Companies
- Development of advanced prototypes and simulators: tools for testing and validating AI algorithms in simulated environments that replicate real maritime conditions
05
Design and implementation of deep neural networks for autonomous control systems and predictive management in intelligent ships
- Fundamentals of Deep Neural Networks: Multilayer Perceptrons, Activation Functions, Backpropagation and Optimisation
- Advanced architectures for maritime systems: CNN, RNN, LSTM and Transformers adapted to naval transport
- Design of models for autonomous control systems: integration of neural networks in predictive control based on models (MPC) applied to intelligent ships
- Supervised and unsupervised learning in marine data: pre-processing of sensory signals, anomaly detection and clustering of operational behaviours
- Implementation of networks for predictive management: forecast of sea conditions, predictive maintenance of machinery and energy optimisation
- Development and integration of intelligent perception systems: sensory fusion through deep learning for safe navigation and autonomous handling in dynamic environments
- Hyperparameter training and adjustment techniques: regularization, dropout, data augmentation techniques specific to maritime data
- Platforms and tools for real-time deployment: TensorFlow, PyTorch, ONNX, and deployment on shipped hardware
- Evaluation and validation of models: performance metrics, tests on maritime simulators and real test scenarios
- Safety and robustness considerations: mitigation of adversary attacks, fault tolerance and reliability in naval autonomous systems
06
Autonomous control systems, perception and cybersecurity for intelligent ships with deep neural networks and predictive analysis
- Fundamentals and architecture of autonomous control systems in ships: dynamic modelling, embedded systems and maritime communication protocols
- Deep neural networks for maritime perception: design, training and optimisation of CNN and RNN applied to optical sensors and radar
- Advanced sensory fusion: LiDAR data integration, multi-beam sonar and hyperspectral cameras for navigation and detection of obstacles in complex maritime environments
- Predictive analysis algorithms for predictive maintenance and operational optimisation: using statistical models and machine learning to anticipate failures in critical naval systems
- Implementation of ADAS (Advanced Driver Assistance Systems) adapted to intelligent ships for autonomous and semi-autonomous manoeuvres with built-in safety protocols
- Cybersecurity architecture and protocols: defence against cyber attacks, integrity assurance in SCADA systems and access management in ship networks
- Detection and mitigation of cyber threats using machine learning techniques: real-time network traffic analysis and adaptive response to intrusions
- International regulations and standards related to autonomy and cybersecurity in naval transport: IMO, IEC 62676 and IEC 61162-460
- Integration of autonomous systems with cloud-based control platforms and Edge Computing for fleet operations and remote monitoring
- PILOTS cases and simulations of autonomous navigation: implementation of neural networks for route optimisation, response to adverse weather conditions and collabourative management of maritime traffic
07
Advanced strategies for optimisation, autonomous control and cybersecurity on smart ships with neural networks and predictive analysis
- Fundamentals of advanced optimisation for naval systems: classical techniques, metaheuristics and evolutionary algorithms applied to the operating efficiency of intelligent ships
- Design and implementation of autonomous control in ships - systems architecture, integrated sensors and intelligent actuators for precise and safe manoeuvres
- Deep neural networks applied to the prediction of maritime conditions and system performance: supervised and unsupervised models, training, validation and deployment in real environments
- Predictive maintenance analysis based on on-board sensor data: early detection of failures, prognostics and intervention planning to reduce costs and downtime
- Adaptive and robust control algorithms for autonomous navigation: response to dynamic disturbances and adverse environmental conditions
- Integration of maritime cyberphysical systems: secure communication between sensors, control platforms and command centres in real time
- Naval transport cybersecurity protocols and standards: threat assessment, vulnerability management and risk mitigation in smart ships
- Application of machine learning techniques for detection and response to cyber attacks: early warning systems and self-defence in networks of connected ships
- Advanced simulation and digital twins for route optimisation and operations in autonomous fleets - scenario modelling and impact analysis on performance and security
- International regulations and best practices for the adoption of artificial intelligence in the naval sector - legal compliance, ethics and technological governance
08
Integration of distributed AI systems and digital twins for predictive management and optimisation in autonomous naval fleets
- Fundamentals of distributed AI: distributed architectures, communication models, interoperability protocols and security in multi-agent systems applied to the naval field
- Design and development of digital twins for boats: dynamic modelling, calibration with real-time sensor data and predictive simulation of structural and operational behaviour
- Integration of heterogeneous data: fusion of information from IoT sensors, radars, SCADA systems and AIS platforms for continuous feeding of predictive models
- Machine learning and reinforcement algorithms for route optimisation, energy consumption, and intelligent resource allocation in autonomous fleets
- Definition and application of performance and resilience metrics in maritime AI distributed systems: key indicators for predictive maintenance and automated decision making
- Advanced protocols for secure communication and synchronisation between embarked AI nodes and onshore platforms: blockchain, cryptography and digital identity management
- Implementation of adaptive control systems based on digital twins for real-time management of faults, early warnings and mitigation of operational risks
- Case study: Practical application in autonomous fleet simulators, integration with port management systems and reduction of the environmental footprint through AI
- Design of scalable and redundant architectures for deploying AI in highly complex maritime environments and adverse conditions
- Advanced analysis of results and continuous optimisation through feedback loop between digital twins and AI systems, ensuring constant improvement of efficiency and safety
09
Design and implementation of autonomous navigation systems, multisensory perception and cybersecurity in intelligent ships with deep neural networks and digital twins
- Advanced fundamentals in design of autonomous navigation systems for intelligent ships: modular architecture, redundancy and fault tolerance.
- Integrated multisensory perception: LiDAR, radar, hyperspectral, sonar and GNSS data fusion for real-time 3-dimensional mapping.
- Modelling and processing of sensory signals through deep neural networks: CNN, RNN and hybrid architectures for detection and classification of objects in complex maritime environments.
- Practical implementation of digital twins in naval transport: Predictive simulation, real-time monitoring and operational optimisation using reinforced learning techniques.
- Adaptive Autonomous Control Algorithms: Optimal route planning, collision avoidance and decision making under uncertainty using probabilistic models and Deep Reinforcement Learning.
- Integration of cyberphysical systems for the safety of smart ships: secure communication protocols, intrusion detection and automatic response to cyberattacks through artificial intelligence.
- Cryptography applied to maritime communications networks: Implementation of blockchain and advanced encryption techniques to guarantee the integrity and confidentiality of critical data.
- International regulations and technical standards for autonomous navigation and naval cybersecurity: SOLAS compliance, IMO, IEC 62998 and IMO recommendations on autonomous systems.
- Development and integration of intelligent man-machine interfaces: Augmented reality for remote monitoring and assisted control in navigation and manoeuvreing operations.
- PILOTS Case and Project Studies: Design and simulation of complete systems of autonomous navigation and cyber resilience applied to state-of-the-art naval and commercial platforms.
10
Ethics, regulation and governance of AI in maritime transport
- Regulatory framework of IMO autonomous ships (Mass Code)
- European Artificial Intelligence Regulation
- Legal responsibility of autonomous systems
- Algorithmic decision making ethics
- Explainability and transparency of models
- Maritime data quality and bias
- Certification of AI systems by classification companies
- Human Factor and Remote Monitoring
- Data protection and privacy
- AI Responsible Adoption Strategies
11
Master’s Final Project in Artificial Intelligence applied to Naval Transport
- Comprehensive architecture of autonomous systems in ships: sensors, actuators, control systems and communication networks in the maritime environment
- Advanced autonomous navigation models: route planning algorithms, obstacle avoidance and real-time decision making under dynamic sea conditions
- Digital twins applied to naval management: conceptualization, design, simulation and validation to replicate the operational and structural behaviour of autonomous fleets
- Integration of artificial intelligence for multidimensional optimisation: deep learning, neural networks and optimisation algorithms applied to energy consumption, predictive maintenance and operational efficiency
- Maritime communications platforms for autonomous fleets: protocols, latency, redundancy and security in data transmission between boats and control centres
- Cybersecurity in autonomous naval transport systems: risk assessment, specific attacks (spoofing, malware, intrusion), and defence strategies through applied artificial intelligence
- International regulations and technical standards for the safe operation of autonomous fleets: IMO compliance, SOLAS, and Cybersecurity recommendations
- Implementation of real-time monitoring systems: big data analysis, anomaly detection and generation of alerts for decision making at a tactical and strategic level
- Architecture and management of the centralised control platform for autonomous fleets - hardware-software integration, redundancy, scalability and interoperability protocols
- Development of advanced simulation scenarios using digital twins: evaluation of navigation strategies, emergency response and optimisation of resources in the autonomous maritime operation
Workload
Study Time and Workload
1500 hours of study, online and at your own pace. Move the slider to the hours a week you can really put in.
Hours a week8
Weeks to finish—
You finish around—
Who it is for
Naval and marine engineers looking to lead the digital transformation of the sector with AI.Merchant Marine officers and captains interested in optimizing navigation and safety with intelligent systems.Logistics and port management professionals who want to revolutionize operational efficiency with predictive analytics and automation.Software developers and data scientists who aspire to appl
Faculty
Academic direction and faculty
The teaching team at Navalis: working professionals who deliver and oversee the institute's programmes.
Eng. Diego Cifuentes
Cap. Javier Abaroa (MCA)
Dr. Pau Ferrer
Dr. Nuria Llobregat
Eng. Javier Bañuls
Eng. Sofía Marquina
Eng. Tomás Riera
Verified reviews
Student Reviews
Reviews collected and verified by Opynio, not written by us.
Outcomes
Learning Outcomes
- 01Optimize fleet management
- 02Develop autonomous navigation systems
- 03Implement predictive models for preventive maintenance of vessels
- 04Improving energy efficiency in maritime transport
- 05Predict and mitigate operational risks in real time
- 06Design and implement intelligent cargo management systems
Careers
Career Opportunities
01```html
02Naval Route Optimization Algorithm Developer: improving efficiency, reducing fuel consumption, and minimizing environmental impact.
03Autonomous Navigation Systems Specialist for Ships: design, implementation, and testing of intelligent navigation and control systems.
04Real-Time Data Analyst for Maritime Traffic Management: congestion prediction, safety optimization, and improved port efficiency.
05Artificial Intelligence Consultant for the Naval Industry: advising on the adoption of AI solutions to optimize operations and improve decision-making.
06Researcher in the Development of New AI Algorithms for Naval Applications: exploring new machine learning techniques and their application to maritime problems.
07AI Software Engineer for Ship Control Systems: software development for task automation and improved onboard safety.
08Cybersecurity Specialist for Intelligent Navigation Systems: protecting autonomous navigation systems against cyberattacks and vulnerabilities.
09AI Innovation Project Manager for the Naval Sector: planning, execution, and monitoring of research and development projects for AI solutions for the naval industry.
Questions
Entry Requirements
Naval Transport
Yes. The itinerary includes ECDIS/Radar-ARPA/BRM with harbor, ocean, fog, storm, and SAR scenarios.
Online with live sessions; hybrid option for simulator/practical placements through agreements.
Maritime or naval transport sector.
Recommended functional SMCP. We offer support materials for standard phraseology.
Yes, with a relevant degree or experience in maritime/port operations. The admissions interview will confirm suitability.
Optional (3–6 months) through Companies & Collaborations and the Alumni Network.
Simulator practice (rubrics), defeat plans, SOPs, checklists, micro-tests and applied TFM.
A degree from Navalis Magna Institute + operational portfolio (tracks, SOPs, reports and KPIs) useful for audits and employment.
Online admissions
Admissions Process
No waiting for a phone call. If your documents are complete, you can pay in the same session.
Entry requirements
```htmlAcademic/Professional Profile:Bachelor's/Undergraduate degree in Computer Engineering, Mathematics, Physics, Telecommunications Engineering, or a related field; or demonstrable professional experience in the field of Artificial Intelligence or Naval Transport.Technical Skills:Basic knowledge of programming (Python, R), statistics, linear algebra, and calculus. Desirable familiarity with machine learning frameworks (TensorFlow, PyTorch).Language proficiency:Technical English level (B2 or higher) for understanding scientific and technical documentation.Documentatio
Questions?
Frequently Asked Questions
Leave your details and admissions will write back. If you already know this is the one, go straight to the application and pay in the same session.