Master’s Degree in Automation of Intelligent Ships
Why this master’s programme?
The Master in Smart Ship Automation
This program prepares you to lead the digital transformation in the maritime industry. Learn to design, implement, and maintain state-of-the-art automated systems, optimizing the efficiency, safety, and sustainability of naval operations. Master the integration of sensors, AI, and robotics in fleet management and the operation of autonomous vessels. This program provides you with the skills needed to drive innovation and the future of navigation.
Differentiating Advantages
- Development of Real-World Projects: Implement automation solutions in simulated environments and case studies.
- Industry Experts: Learn from leading professionals in naval automation and maritime technologies.
- Cutting-Edge Tools and Technologies: Use state-of-the-art software and hardware in ship automation.
- Comprehensive Vision: Understand the impact of automation on safety, the environment, and risk management.
- Professional Networking: Connect with companies and professionals in the maritime and technology sectors.
- Modality: Online
- Level: Masters
- Hours: 1600 H
- Start date: 26-04-2026
Availability: 1 in stock
Who is it aimed at?
- Naval and Marine Engineers who want to lead the digital transformation of the maritime industry.
- Automation and Control Engineers interested in applying their knowledge to the forefront of naval technology.
- Engineering Officers and Chief Engineers who seek to master the latest generation of automated systems.
- Shipping companies and shipyards that need trained professionals for the implementation and maintenance of smart ships.
- Researchers and academics who aspire to develop innovative solutions for naval automation.
Flexibility and constant updates
Designed for working professionals: online format flexible, updated content with the latest trends in automation and smart ships.
Objectives and skills
Optimizing the energy efficiency of ships:
“Implement onboard energy management systems and monitor consumption in real time, adjusting operating parameters to minimize fuel use without compromising safety and regulatory compliance.”
Implement predictive control systems for equipment maintenance:
Develop predictive models based on historical data, telemetry, and vibration analysis to optimize maintenance plans and reduce unscheduled downtime.
Develop and integrate autonomous navigation systems:
“Implement optimized route planning, considering environmental conditions, energy consumption and operational constraints, validating with simulation and tests in a real environment.”
To efficiently and safely manage onboard propulsion and power generation systems:
“Operate and maintain the propulsion plant, ensuring its availability and optimal performance, interpreting technical manuals and applying emergency procedures safely and efficiently.”
Diagnosing and troubleshooting complex problems in automated ship systems:
“Using advanced diagnostic tools, communication protocols and technical drawings, restoring system operability quickly and safely.”
Supervise and coordinate work teams in naval automation projects:
“Ensuring proper execution, compliance with deadlines and regulations, optimizing resources and fostering a collaborative and safe environment.”
Study plan – Modules
- Fundamentals of control systems in naval automation: types, characteristics, and specific applications in smart ships
- Mathematical modeling and dynamic simulation of maritime processes: advanced techniques for replicating real-world conditions and optimizing energy control
- Model-based predictive control (MPC) applied to the efficient management of propulsion and auxiliary systems
- Implementation of adaptive and robust algorithms for compensating for marine disturbances and load variability
- Integration of smart sensors and maritime industrial communication protocols: CAN bus, Modbus, IEC 61162, and IoT networks for real-time data collection and analysis
- Design and optimization of onboard energy management systems (EMS): strategies for reducing consumption and pollutant emissions
- Distributed and decentralized control on offshore platforms: synchronization and coordination of subsystems for maximum operational efficiency
- Application of artificial intelligence and machine learning Dynamic adjustment of control parameters to anticipate changing conditions
Stability and safety analysis in automated systems: failure prevention, early anomaly detection, and emergency protocols
International regulations and technical standards (IMO, ISO, IMO MEPC) related to automation and energy efficiency in the shipbuilding industry
Case studies and real-time simulations: design, implementation, and validation of advanced control strategies in smart ships
- Fundamentals of Cyber-Physical Systems (CPS): Definition, Architecture, and Components in Smart Maritime Environments
- Design and Architecture of IoT Networks for Maritime Applications: Protocols, Topologies, and Standardization
- Integration of Smart Sensors and Actuators: Real-Time Data Acquisition Technologies for Autonomous Vessels
- Maritime Communication Platforms: Satellite Networks, 5G, LPWAN, and Their Impact on Telemetry and Remote Control
- IoT Communication Protocols Specific to Maritime Environments: MQTT, CoAP, OPC UA, and Their Implementation in Smart Vessels
- Modeling and Simulation of Cyber-Physical Systems for Predictive Monitoring: Advanced Digital Twin Techniques and Digital Twins
- Predictive Control Algorithms Based on Machine Learning and Big Data Analytics for Performance Optimization and Maintenance in Autonomous Vessels
- Integration of SCADA systems and onboard systems for the centralized control and monitoring of automated platforms
Cybersecurity in maritime CPS: vulnerability assessment, protection protocols, and resilience strategies in IoT networks
Implementation of distributed architectures and edge computing for latency reduction and reliability improvement in autonomous operations
Advanced diagnostics and predictive maintenance based on the analysis of vibrations, temperature, and other critical parameters using IoT
Case studies of CPS and IoT integration in the real-time monitoring of propulsion, power, and autonomous navigation systems
International regulations and standards applicable to cyber-physical systems and IoT networks in the maritime industry
Development of intelligent human-machine interfaces (HMIs) for the secure and efficient remote control of autonomous vessels
Future trends and technological challenges in the convergence of CPS, IoT, and automation advanced maritime
- Fundamentals of Automatic Control Systems in Smart Ships: Principles, Types of Control, and Distributed Architecture
- Dynamic Modeling of Naval Systems: Equations of Motion, Hydraulic and Thermal Modeling, Real-Time Simulations
- Advanced Predictive and Adaptive Control Strategies for Optimizing Energy Consumption in Engines and Auxiliary Systems
- Integration of Smart Sensor Networks: Critical Variables, Advanced Sensors, Industrial Communication Protocols, and Standardization
- Real-Time Monitoring: SCADA Platforms, Data Analysis, Anomaly Detection, and Predictive Diagnostics Based on Artificial Intelligence
- Energy Optimization Through Multivariable Control and Integrated Management of Onboard Power Demand
- Redundant Systems and Fault-Tolerant Architecture to Ensure Continuous Operation in Harsh Maritime Environments
- Cybersecurity in Naval Control Systems: Threat Analysis, Defense Protocols, Authentication, and Data Encryption Critical factors
- International regulations and technological standards applicable to automation and cybersecurity in autonomous vessels
- Practical implementation: case studies, advanced control simulators, and evaluation of operational results in real and virtual environments
- Fundamentals of Integrated Architecture: Modular Design and Scalability in Autonomous Navigation Systems
- Advanced Sensor Components: LiDAR Sensors, High-Resolution Radar, Multispectral Cameras, and Inertial Sensors
- Integration and Fusion of Sensor Data: Real-Time Processing Algorithms and Machine Learning Techniques for Environmental Perception
- Maritime Digital Twins: Conceptualization, Modeling, and Dynamic Simulation of Naval Systems
- Implementation of Digital Platforms for Remote Monitoring: Communication Protocols, Latency, and Data Synchronization
- Predictive Maintenance Based on Data Analysis: Early Failure Detection Using Neural Networks and Multivariate Statistical Analysis
- Advanced Diagnostic Systems: Use of Artificial Intelligence for Signal Interpretation and Early Warning of Operational Anomalies
- Cyber-Physical Architecture for Smart Ships: Hardware-Software Integration and Cybersecurity Management in maritime environments
International regulations and standards applicable to the automation and monitoring of smart ships
Case studies of the implementation and validation of digital twins in predictive maintenance and operational optimization in autonomous fleets
- Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML): Key Concepts, Supervised, Unsupervised, and Reinforcement Algorithms Applied to Naval Systems
- Architecture and Design of Intelligent Ship Systems: Integration of Sensors, Actuators, and Autonomous Control Systems with AI
- Deep Neural Networks and Deep Learning: Applications in Pattern Recognition for Navigation and Predictive Maintenance
- Multisensor Data Processing and Fusion in Maritime Environments: LiDAR, Radar, Cameras, Sonar, and GNSS for Autonomous Perception and Decision Making
- Intelligent Route Planning and Optimization Algorithms: Dynamic Adaptation to Weather Conditions, Shipping Traffic, and Operational Constraints
- AI-Based Collision Detection and Avoidance Systems: Real-Time Interpretation of AIS, Radar, and VTS Data to Ensure Safety in Restricted and Congested Waters
- Machine Learning for Maintenance Predictive and lifecycle management of critical equipment: analysis of vibrations, temperature, and failure patterns to minimize downtime.
Distributed AI architectures and edge computing in autonomous vessels: design of resilient and efficient networks for local data processing and latency reduction.
Advanced simulation models and naval digital twins for training, testing, and validating AI algorithms in controlled environments.
Cybersecurity applied to AI systems on ships: anomaly detection, defense against adversarial attacks, and protection of critical data integrity.
International regulations and standards for the implementation of AI in naval automation: compliance with IMO and SOLAS regulations and guidelines for autonomous systems.
Real-world case studies and cutting-edge projects: detailed analysis of operational smart ships and prototypes, technical challenges, and mitigation strategies.
Development and implementation of explainable AI (XAI) systems and transparency in autonomous decision-making to ensure trust. and auditing in maritime operations
Human-machine interaction in smart bridges: optimized interfaces for remote monitoring, hybrid control, and emergency response
Innovations in smart sensors and advanced navigation systems: integrating emerging technologies to increase the ship’s predictive and adaptive capabilities
- Fundamentals of Artificial Intelligence in Maritime Systems: definition, types, and specific applications in smart ships
- Machine Learning for Prediction and Diagnosis: supervised, unsupervised, and reinforcement algorithms applied to fault detection and predictive maintenance
- Deep Neural Networks and Deep Learning: structure, training, and use in pattern recognition for autonomous navigation
- Computer Vision and Image Processing: integration of optical sensors and LIDAR systems for obstacle identification and safe maneuvering
- Multi-objective Optimization Algorithms: planning efficient routes considering energy consumption, weather conditions, and maritime traffic
- AI-Based Adaptive Control Systems: design of intelligent controllers for stabilization and dynamic response under varying conditions
- Implementation of Distributed AI Systems: coordination and communication between autonomous subsystems on board for operations Joint and redundancy
Cybersecurity and Resilience in Intelligent Platforms: detection and response mechanisms against cyberattacks using AI techniques
Big Data and Predictive Analytics in Maritime Operations: massive data collection, storage, and processing for real-time decision-making
International Regulations and Standards for AI in Autonomous Vessels: regulatory compliance, certification, and best practices for intelligent marine systems
Simulation and Virtual Training: development of digital environments for testing and validating AI models under diverse maritime conditions
Case Studies and Implementation Studies: detailed analysis of real-world projects, technical challenges, and results obtained in ship automation
- Fundamentals of Autonomous Control Systems: Architecture, Automation Levels, and Industrial Communication Protocols
- Design and Implementation of Distributed Control Systems (DCS) and Supervisory Control and Data Acquisition (SCADA) Systems on Smart Ships
- Integration of Advanced Sensors and Intelligent Actuators: LiDAR, High-Resolution Radar, Multispectral Cameras, and Inertial Measurement Units (IMUs)
- Predictive and Adaptive Control Algorithms for Autonomous Navigation in Dynamic Maritime Environments
- Secure Maritime Communication Networks: Enhanced AIS, SATCOM, Mesh Networks, and Low-Latency Protocols
- Advanced Principles and Practices of Marine Cybersecurity: Identity Management, Access Control, and Network Segmentation on Board
- Protection Against Specific Cyber Threats: Malware, Phishing, DDoS Attacks, and Vulnerabilities in Maritime Automation Systems
- Implementation of industrial firewalls, IDS/IPS systems, and encryption techniques for critical ship data
- Predictive maintenance based on artificial intelligence and Big Data analytics: real-time monitoring techniques and early fault detection
- Diagnostic and prognostic models for failures in critical equipment: motors, generators, pumps, and electrical systems
- Application of digital twins for simulation, optimization, and maintenance planning in autonomous vessels
- Comprehensive management of control, cybersecurity, and maintenance systems: audit protocols, traceability, and international regulatory certification
- Case studies of implementation in commercial and naval fleets: lessons learned and best practices at a global level
- Applicable international regulations and standards: IMO, IEC 62443, NIST, and guidelines for smart ships
- Future of automation and cybersecurity in the maritime industry: trends, disruptive innovations, and challenges emerging
- Fundamentals of Energy Optimization in Smart Ships: Comprehensive Thermal, Electrical, and Mechanical Analysis
- Design and Modeling of Hybrid Propulsion Systems: Combining Renewable Sources, Batteries, and Diesel-Electric Engines
- Implementation of Advanced Predictive Control (MPC) Algorithms for Real-Time Energy Management
- Intelligent Monitoring Systems: IoT Sensors, Data Acquisition, and Onboard Industrial Communications
- Optimization Using Digital Twins: Real-Time Simulation for Predictive Maintenance and Operational Adjustments
- Integration of SCADA and PLC Systems for Autonomous Control and Centralized Monitoring of Energy Assets
- Advanced Methodologies for Fault Diagnosis and Self-Adjustment of Operating Parameters in Energy Systems
- Load and Demand Management Strategies: Dynamic Balancing Against Variable Operating Requirements and Conditions Environmental considerations
Industrial communication protocols (OPC UA, Modbus, CAN bus) applied to the interconnection of devices and systems in the maritime environment
Cybersecurity in autonomous control: protection against cyberattacks in intelligent energy systems and their impact on operational safety
Implementation of artificial intelligence for adaptive optimization based on machine learning and big data analytics
International regulations and standards applicable to automation and energy control in next-generation vessels
Case studies and advanced simulations for the continuous improvement of energy efficiency and the reduction of pollutant emissions
Advanced human-machine interface (HMI) for the intuitive monitoring and operation of complex systems
Development of integrated condition-based maintenance (CBM) plans supported by intelligent technologies and predictive analytics
- Energy storage systems and their dynamic management to optimize the vessel’s operational autonomy and resilience
- Real-world case studies of successful implementation in smart ship projects with optimized energy systems
- Advanced Fundamentals in Control System Design for Smart Ships: Principles of Distributed Control, Real-Time Control, and Redundancy in Maritime Architectures
- Integration of Smart Sensors and Actuators: Use of Maritime IoT, CAN Bus Networks, NMEA 2000 and OPC UA Protocols for Interoperability and Data Synchronization
- Development and Implementation of Model Predictive Control (MPC) Algorithms for Energy Management and Efficient Propulsion in Autonomous Vessels
- SCADA and PLC Platforms Specific to Naval Applications: Configuration, Programming, and Optimization of Critical Onboard Processes
- Application of International Standards in Maritime Cybersecurity: Compliance with the IMO Guidelines on Maritime Cyber Risk Management and IEC 62443 Standards
- Security Architecture for Integrated Systems: Network Segmentation, Industrial Firewalls, Intrusion Detection Systems (IDS/IPS), and Techniques of
- Marine Hardware Hardening
- Evaluation and Mitigation of Specific Vulnerabilities in Autonomous Vessel Control Systems: Threat Analysis and Cybersecurity Risk Management
- Implementation of Secure Communication Protocols for Smart Vessels: Encryption, Multi-Factor Authentication, and VPN Uses in Satellite and Maritime Networks
- Advanced Methodologies for Predictive Maintenance: Vibration Analysis, Infrared Thermography, and Machine Learning Applied to Propulsion Systems and Naval Machinery
- Big Data and Real-Time Analytics: Collection, Processing, and Visualization of Operational Data to Anticipate Failures and Optimize Component Lifespan
- Integration of Digital Twins in Maintenance Management and Simulation of Operational Scenarios for Predictive Decision-Making
- Remote and Automated Inspection Procedures: Drones, ROVs, and Continuous Monitoring Systems to Ensure the Structural and Functional Integrity of the Vessel
- Lifecycle management of control and maintenance systems: planning, documentation, audits, and compliance with SOLAS and IMO regulations
- Case studies and advanced simulations: resolving cyber incidents, intelligent system failures, and rapid recovery strategies in autonomous maritime environments
- Emerging technological innovations: explainable artificial intelligence (XAI) in maritime control, blockchain for maintenance traceability, and adaptive systems in smart ships
- Advanced design of integrated control systems for smart ships: distributed architecture, maritime communication protocols, and operational redundancy
- Implementation of predictive and adaptive control algorithms: dynamic modeling of naval systems and real-time optimization of autonomous operations
- Fundamentals and applied techniques in cybersecurity for maritime systems: vulnerability analysis, secure communication protocols, and prevention of cyberattacks
- Development of strategies for the detection and mitigation of internal and external threats in naval automation environments
- IoT-based predictive maintenance systems: smart sensors, real-time data acquisition, and failure analysis using machine learning
- Integration of SCADA platforms and remote monitoring systems for continuous monitoring of the ship’s condition and its subsystems
- International regulations and technical standards in naval automation and cybersecurity: compliance with SOLAS, IMO, and applicable ISO standards
- Design and implementation of autonomous management systems for safe navigation: route planning, energy optimization, and dynamic risk assessment
- Advanced simulation and computational modeling methodologies to validate the behavior of autonomous systems under variable maritime conditions
- Development of human-machine interfaces (HMIs) for efficient monitoring and control, including augmented reality and intelligent alerting systems
- Case studies of systems integration and performance evaluation in smart ships: real-world projects and experimental simulations
- Preparation and presentation of the final master’s thesis integrating knowledge in automatic control, cybersecurity, and predictive maintenance for the autonomous management of smart ships, with a focus on technological innovation and industrial application
Career prospects
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- Naval Automation Engineer: Design, implementation, and maintenance of automated systems on ships.
- Control Systems Specialist: Optimization and management of propulsion, power, and safety control systems.
- Maritime Technology Consultant: Advising on the adoption of smart technologies and automation in the naval industry.
- Smart Ship Software Developer: Creation of applications and systems for the management and control of autonomous ships.
- Naval Automation Researcher: Development of new technologies and solutions for ship automation.
- Automation Project Manager: Planning and execution of automation projects in ship construction and modernization.
- Maritime Cybersecurity Specialist: Protection of automated ship systems against Cyber threats.
- Automated Systems Technical Inspector: Verification and certification of automated systems on ships.
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Entry requirements
Academic/professional profile:
Bachelor’s degree in Nautical Science/Maritime Transport, Naval/Marine Engineering or a related qualification; or proven professional experience on the bridge/in operations.
Language proficiency:
Functional Maritime English (SMCP) recommended for simulations and technical materials.
Documentation:
Updated CV, copy of qualification or seaman’s book, national ID/passport, motivation letter.
Technical requirements (for online):
Device with camera/microphone, stable internet connection, monitor ≥ 24” recommended for ECDIS/Radar-ARPA.
Admissions process and dates
Online
application
(form + documents).
Academic review and interview
Admissions decision
Admissions decision
(+ scholarship offer if applicable).
Place reservation
(deposit) and enrolment.
Induction
(access to the virtual campus, calendars, simulator guides).
Scholarships and financial support
- Advanced Automation: Master the latest technologies in autonomous ship systems, including intelligent control, navigation, and propulsion.
- Operations Optimization: Learn how to improve the efficiency, safety, and sustainability of maritime operations through automation and data analysis.
- Design and Integration: Develop skills to design and integrate automated systems on new and existing ships, complying with international regulations.
- Simulation and Modeling: Use simulation tools to model and validate the performance of automated systems under different operating conditions.
- Artificial Intelligence and Machine Learning: Apply AI and ML techniques for autonomous decision-making, predictive maintenance, and energy consumption optimization.
Testimonials
During my Master’s degree in Intelligent Ship Automation, I developed a route optimization algorithm that, when simulated, demonstrated a 12% reduction in fuel consumption and an 8% increase in operational efficiency compared to traditional systems. This project allowed me to apply my acquired knowledge in autonomous control, data analysis, and artificial intelligence, and positioned me as an expert in the field of intelligent navigation.
During the Master’s in Maritime Robotics and Automation, I developed an autonomous control system for an ROV for inspecting oil platforms, which reduced inspection time by 30% and improved the accuracy of data collection by 15%, results that were validated in simulations and tank tests.
“This master’s degree provided me with the tools and knowledge necessary to lead the development and implementation of an autonomous navigation system for cargo ships. Thanks to the training I received, I was able to optimize navigation routes, reducing fuel consumption by 15% and significantly minimizing environmental impact. Currently, the system is being implemented on a fleet of 20 ships, demonstrating the effectiveness and scalability of the solution I designed.”
I applied the knowledge from the Master’s in Intelligent Ship Automation to develop an autonomous navigation system that reduced fuel consumption by 12% and optimized the routes of my company’s fleet, generating significant savings in operating costs and a decrease in our carbon footprint.
Frequently asked questions
Autonomous vessels or vessels with high levels of automation, including merchant ships, ferries, recreational craft and offshore platforms.
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.
In both cases, encompassing the automation of existing ships and the design of new autonomous ships.
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 University + operational portfolio (tracks, SOPs, reports and KPIs) useful for audits and employment.
- Advanced design of integrated control systems for smart ships: distributed architecture, maritime communication protocols, and operational redundancy
- Implementation of predictive and adaptive control algorithms: dynamic modeling of naval systems and real-time optimization of autonomous operations
- Fundamentals and applied techniques in cybersecurity for maritime systems: vulnerability analysis, secure communication protocols, and prevention of cyberattacks
- Development of strategies for the detection and mitigation of internal and external threats in naval automation environments
- IoT-based predictive maintenance systems: smart sensors, real-time data acquisition, and failure analysis using machine learning
- Integration of SCADA platforms and remote monitoring systems for continuous monitoring of the ship’s condition and its subsystems
- International regulations and technical standards in naval automation and cybersecurity: compliance with SOLAS, IMO, and applicable ISO standards
- Design and implementation of autonomous management systems for safe navigation: route planning, energy optimization, and dynamic risk assessment
- Advanced simulation and computational modeling methodologies to validate the behavior of autonomous systems under variable maritime conditions
- Development of human-machine interfaces (HMIs) for efficient monitoring and control, including augmented reality and intelligent alerting systems
- Case studies of systems integration and performance evaluation in smart ships: real-world projects and experimental simulations
- Preparation and presentation of the final master’s thesis integrating knowledge in automatic control, cybersecurity, and predictive maintenance for the autonomous management of smart ships, with a focus on technological innovation and industrial application
Request information
Complete the Application Form.
Attach your CV/degree certificate (if you have it to hand).
Indicate your preferred cohort (January/May/September) and whether you would like the hybrid option with simulator sessions.
An academic advisor will contact you within 24–48 hours to guide you through the admission process, scholarships, and compatibility with your professional schedule.
Faculty
Eng. Tomás Riera
Full Professor
Eng. Tomás Riera
Full Professor
Naval engineer specializing in the selection, integration, and optimization of propulsion systems for yachts, high-speed craft, and service vessels.
Eng. Sofía Marquina
Full Professor
Eng. Sofía Marquina
Full Professor
Materials engineer specializing in marine composites and corrosion protection systems for ships, yachts, and port structures.
Eng. Javier Bañuls
Full Professor
Eng. Javier Bañuls
Full Professor
Naval electrical engineer with over fifteen years of experience in the design, integration, and operation of power and automation systems.
Dr. Nuria Llobregat
Full Professor
Dr. Nuria Llobregat
Full Professor
Specialist in meteorological and operational decision-making for coastal and offshore navigation, integrating wind, wave, and current models.
Dr. Pau Ferrer
Full Professor
Dr. Pau Ferrer
Full Professor
Naval engineer with a PhD in applied hydrodynamics and over a decade of experience designing high-performance hulls and marine structures.
Cap. Javier Abaroa (MCA)
Full Professor
Cap. Javier Abaroa (MCA)
Full Professor
MCA-certified captain with command of ocean-going vessels, specializing in advanced maneuvering, navigation management, and bridge safety.