Master’s Degree in Artificial Intelligence Applied to the Navy
Why this master’s programme?
The Master’s in Artificial Intelligence Applied to the Navy
Prepares you to lead the digital transformation of the maritime sector. Learn to implement AI solutions in key areas such as route optimization, predictive maintenance of vessels, process automation, and maritime safety. This program combines theory with practice, using cutting-edge tools and real-world case studies so you can apply your knowledge from day one.
Key Advantages
- Focus on the Maritime Industry: Sector-specific use cases and projects.
- Development of Practical Skills: Implementation of AI algorithms and models.
- Industry Experts: Professors with experience in AI and the shipbuilding industry.
- State-of-the-art Tools: Access to leading AI software and platforms.
- Networking: Connections with companies and professionals in the sector.
- Modality: Online
- Level: Masters
- Hours: 1600 H
- Start date:
Availability: 1 in stock
Who is it aimed at?
- Naval and maritime engineers seeking to apply AI techniques in the design, operation, and maintenance of ships and marine infrastructure.
- Merchant and military marine officers interested in optimizing navigation, safety, and decision-making through intelligent systems.
- Software developers and IT consultants wanting to specialize in AI solutions for the shipbuilding industry and port management.
- Researchers and academics wishing to delve deeper into the use of AI for modeling, simulation, and automation of marine processes.
- Offshore and energy industry professionals seeking to improve efficiency and safety in the exploration and exploitation of marine resources through AI.
Study Flexibility
Adapted to working professionals: flexible online format, updated content and personalized tutoring to boost your professional development.
Objectives and skills
Optimize resource management and naval logistics:
“Implement predictive and corrective maintenance systems to minimize downtime and optimize the life cycle of naval equipment.”
Develop systems for detecting and responding to maritime threats:
Integrate data from multiple sources (radar, AIS, sonar, cameras) to create a complete and accurate situational picture, assessing the credibility of the information and prioritizing threats based on their probability and potential impact.
Design and simulate complex operational scenarios for strategic decision-making:
“Modeling maritime traffic flows, extreme weather conditions, and critical system failures, integrating real-time data and predictive analytics to optimize routes and resources.”
Automating the analysis of oceanographic data to improve the prediction of weather patterns:
Develop robust predictive models, validated with historical data and updated in real time, integrating machine learning and visualization tools to identify significant climate trends and communicate results effectively to experts and the general public.
Implement AI algorithms for optimizing navigation routes and fuel consumption:
“Manage parameters (wind, current, draft) and restrictions (ETA, prohibited zones) with predictive consumption models and dynamic route optimization.”
Creating predictive models for the preventive maintenance of naval systems:
Integrate sensor data, failure history, and operational conditions to predict failures and optimize maintenance programs, minimizing downtime.
Study plan – Modules
- Mathematical Foundations of Machine Learning Algorithms Applied to Naval Systems: Optimization Theory, Bayesian Statistics, and Multivariate Data Analysis
- Design and Development of Predictive Models for Efficient and Safe Navigation in Complex Maritime Environments Using Supervised and Unsupervised Learning
- Deep Neural Networks (Deep Learning) and Their Implementation in Maritime Pattern Recognition, Anomaly Detection, and Prediction of Adverse Weather Conditions
- Reinforcement Learning Algorithms for Autonomous Decision-Making in Naval Operations, Including Evasive Maneuvers and Real-Time Resource Management
- Multivariate Optimization for Logistics Planning and Maritime Routes: Advanced Linear, Integer, and Metaheuristic Programming Techniques Applied to Cost and Time Reduction
- Integration of AI Systems with AIS, GNSS, Radar, and LiDAR Sensors to Improve Situational Awareness and Response in Security Scenarios
- Implementation of federated learning models for cooperation between naval units while maintaining the privacy and security of shared information
- Risk assessment using predictive analytics and probabilistic models for the proactive management of maritime incidents and cyber threats in critical systems
- Advanced techniques for preprocessing and enriching marine data: handling missing data, normalization, outlier detection, and multisensor fusion
- Deployment and maintenance of machine learning models on naval edge computing platforms and in the cloud to support real-time operations
- Study of real-world cases and simulations in virtual environments to validate and improve AI algorithms applied to naval operations and maritime security
- Regulatory, ethical, and cybersecurity aspects of AI integration in naval systems: international regulations and protection of critical infrastructure
maritime
- Fundamentals of Intelligent Systems: Definitions, Architectures, and Evolution in Naval Environments
- Supervised and Unsupervised Learning Algorithms Applied to Real-Time Threat Detection
- Deep Neural Networks for Pattern Recognition in Radar, Sonar, and Electroacoustic Signals
- Advanced Multisensor Data Processing: Information Fusion to Increase Accuracy and Reduce False Positives
- Implementation of Automated Response Systems: Decision and Execution Frameworks in Critical Maritime Scenarios
- Predictive Analytics Techniques for Anticipating Cyber and Physical Threats on Naval Platforms
- Integration of Artificial Intelligence with Geographic Information Systems (GIS) for Dynamic Maritime Surveillance
- International Protocols and Standards for Interoperability and Security in Naval Intelligent Systems
- Cybersecurity in Detection Systems: Defense strategies, intrusion detection, and failure recovery
Best practices in the operation and maintenance of intelligent systems: ensuring availability and reliability in maritime environments
Case studies of AI implementation in maritime defense and security operations: analysis of results and lessons learned
Development of an ethical and legal framework for the use of artificial intelligence in naval surveillance and response
Resource optimization through intelligent algorithms: dynamic allocation and alert prioritization
Simulation and virtual environments for training in detection and automatic response to naval threat scenarios
Emerging trends in artificial intelligence applied to the navy: from autonomous agents to collaborative defense systems
- Theoretical and mathematical foundations of machine learning algorithms applied to naval navigation: regression, classification, clustering, and neural networks
- Implementation of supervised and unsupervised models for predicting maritime states and critical environmental conditions
- Integration of big data and heterogeneous data sources: remote sensors, AIS systems, meteorology, and oceanography for model training and validation
- Optimization of routes and energy consumption using genetic algorithms and reinforcement learning techniques in dynamic naval scenarios
- Application of deep learning for the automatic detection and classification of obstacles and threats in real time using satellite imagery and radar systems
- Development of early warning systems based on machine learning for collision prevention and maritime safety incidents
- Use of sensory fusion techniques and convolutional neural networks to improve perception in adverse maritime environments
- Risk assessment and mitigation using predictive analytics and stochastic failure models of critical systems in naval operations
- Implementation of edge computing platforms for in-situ data processing and autonomous decision-making in naval units
- Cybersecurity aspects and robustness of artificial intelligence models to ensure integrity and confidentiality in naval communications and control systems
- Fundamentals of Marine Autonomous Systems: Modular Design, Distributed Architectures, and Communication Protocols in Maritime Environments
- Advanced Sensory Perception: LIDAR, RADAR, Multibeam Sonar, and Biomimetic Sensor Technologies for Real-Time Object Detection and Classification
- Multisensory Data Processing and Fusion: Bayesian Filtering, Extended Kalman, Particle, and Deep Learning Algorithms for Strengthening Environmental Information
- Convolutional Neural Networks (CNNs) and Deep Learning Applied to Marine Image Interpretation, Obstacle Detection, and Dynamic Pattern Recognition
- Predictive Modeling and State Estimation: Particle Filters, Hidden Markov Models, and Inference Techniques for Predicting Multi-Target Trajectories
- Adaptive Control of Autonomous Fleets: Distributed Control Architectures, Consensus and Training Algorithms, and Dynamic Resource Management in Maritime Environments
- Tactical coordination and collaborative planning: multi-objective optimization, applied game theory, and negotiation strategies among autonomous marine vehicles
- Simulation and validation in autonomous marine systems: virtual simulation environments, hydrodynamic models, and experimental validation in real testbeds
- Integration of secure communication systems: maritime protocols, encryption, interference resistance, and cybersecurity concepts applied to the autonomous fleet network
- Regulatory and ethical aspects of the deployment of autonomous systems in the navy: international regulations, operational responsibility, and technological risk management
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- Fundamentals of Advanced Data Processing in Naval Systems: Acquisition, Synchronization, and Sensor Calibration
- Types of Maritime Sensors: Doppler Radar, LiDAR, Multibeam Sonar, Inertial Sensors, and High-Accuracy GNSS
- Signal Preprocessing and Filtering in Marine Environments: Noise Reduction and Artifact Removal Techniques in Sensor Data
- Multimodal Sensor Fusion: Algorithms for Integrating Data from Multiple Sources to Improve Environmental and Situational Awareness
- Development of Predictive Models Based on Machine Learning and Deep Learning for Predictive Maintenance of Autonomous Fleets
- Dynamic Fleet Modeling: Simulation and Optimization Under Variable Sea, Traffic, and Weather Conditions Using Stochastic and Deterministic Models
- Implementation of Autonomous Decision-Making Systems Based on Explainable Artificial Intelligence
(XAI) applied to navigation and maneuvering management
Big Data and real-time analytics: architectures and tools for the massive and continuous processing of naval sensor data
Recurrent neural networks and time series models for predicting operational patterns and early detection of fleet anomalies
Integration with command and control (C2) systems: protocols, communication standards, and cybersecurity in connected maritime environments
- Advanced Machine Learning Fundamentals Applied to Naval Optimization: Types of Supervised, Unsupervised, and Reinforcement Learning Algorithms, with Emphasis on Deep Neural Networks and Transfer Learning
- Predictive Modeling for Predictive Maintenance in Naval Systems: Analysis of Data from Onboard IoT Sensors, Extraction of Relevant Features, and Early Fault Detection Techniques Using SVM and Random Forests
- Combinatorial Optimization Algorithms for Maritime Logistics: Application of Evolutionary Methods, Genetic Algorithms, and Metaheuristics in the Efficient Allocation of Routes and Resources
- Intelligent Autonomous Systems: Architecture and Design of Unmanned Marine Vehicles (USVs) with Integration of LIDAR Sensors, Multifrequency Sonar, and Inertial Navigation for Operations Under Adverse Conditions
- Advanced Sensor Fusion for Environmental Perception: Signal Processing and Multisensor Fusion Techniques Based on Extended Kalman Filters and Bayesian Networks to Improve Real-Time Situational Awareness real
- Implementation of autonomous decision-making algorithms: Markovian decision models and deep reinforcement learning for dynamic threat management and tactical evasion in highly complex environments
- Cybersecurity in autonomous maritime systems: Secure communication protocols, intrusion detection through network traffic anomaly analysis, and blockchain techniques to ensure data integrity
- Optimization of energy consumption on naval platforms using artificial intelligence: Recurrent neural networks for energy demand prediction and adaptive control algorithms for hybrid propulsion systems
- Integration of artificial intelligence systems with existing naval infrastructure: Interoperability, communication standards (NMEA 2000, DDS), and middleware development for centralized data management
- Simulation and validation of intelligent models applied to naval tactical scenarios: Use of virtual environments and digital twins for in-silico testing of autonomous algorithms under specific metrics rigorous performance and operational safety standards
- Fundamentals of Machine Learning in the Naval Environment: Types of Algorithms – Supervised, Unsupervised, and Reinforcement Learning
- Maritime Data Processing and Analysis: Acquisition, Cleaning, Normalization, and Labeling of Data from Sensors and AIS Systems
- Design and Implementation of Predictive Models for Early Threat Detection: Collisions, Intrusions, and Adverse Weather Conditions
- Optimization of Autonomous Routes Using Reinforcement Learning Algorithms and Advanced Heuristic Search
- Integration of Deep Neural Networks for Pattern Recognition in Images and Acoustic Signals in Naval Scenarios
- Application of Anomaly Detection Systems for the Cybersecurity of Autonomous Naval Platforms
- Implementation of Computer Vision Techniques and Real-Time Processing for the Identification and Tracking of Maritime Obstacles
- Use of Clustering and Segmentation Algorithms for the Automated Classification of Naval Traffic and Risk areas
Reducing energy consumption through adaptive predictive models for autonomous propulsion systems
Continuous monitoring and evaluation of the performance of Machine Learning models applied to naval operations: updating, recalibration, and bias mitigation
Advanced frameworks and tools for the development and implementation of ML algorithms on naval platforms: TensorFlow, PyTorch, ROS, and other relevant environments
Technical criteria for the validation and verification of intelligent systems in maritime certifications
Operational challenges and best practices in the integration of ML with naval control and automation systems
Real-world cases and advanced studies: detailed analysis of successful projects in the safety and efficiency of autonomous military and commercial fleets
- Fundamentals of Autonomous Systems Design: Modular Architecture, Integrated Sensors, and Communication Protocols in Marine Environments
- Intelligent Navigation Algorithms: SLAM (Simultaneous Localization and Mapping), Inertial Navigation, and Multisensor Fusion for Accuracy in Dynamic Environments
- Modeling and Simulation of Complex Marine Environments Using Artificial Intelligence and Advanced Machine Learning
- Deep Neural Networks Applied to Real-Time Detection, Classification, and Tracking of Maritime Objects
- Development of Predictive Management Systems: Time-Series-Based Predictive Analysis and Supervised Learning to Anticipate Adverse Maritime Conditions
- Integration of Adaptive and Robust Control Systems for Autonomous Maneuvers in the Presence of Environmental Disturbances and Partial Failures
- Optimization of Autonomous Routes Using Genetic Algorithms, Heuristic Optimization, and Optimal Control Under Constraints Maritime and International Regulations
- Communication and Coordination in Autonomous Vehicle Fleets: V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) Protocols for Cooperative Operations
- Risk Assessment and Mitigation Using Vulnerability Analysis Techniques and Extreme Scenario Simulations in Marine Autonomous Systems
- International Regulations and Standards: Legal Compliance, Cybersecurity, and Ethical Considerations in the Deployment of Autonomous Systems in Maritime Defense and Commerce
- Fundamentals of Artificial Intelligence in Maritime Environments: Machine Learning, Deep Learning, and Neural Networks Applied to Naval Systems
- Predictive Models for Logistics Optimization in Naval Operations: Route Optimization, Energy Consumption, and Predictive Maintenance
- Implementation of Naval Autonomous Systems: Distributed Control Architectures, Real-Time Communications, and AI-Based Decision Making
- Computer Vision and Signal Processing for Object Detection and Classification in Complex Maritime Environments
- Application of Autonomous Navigation Algorithms: SLAM (Simultaneous Localization and Mapping), Multi-Sensor Systems, and Data Fusion
- Cybersecurity in Naval Artificial Intelligence: Attack Mitigation, Intrusion Detection, and Proactive Defense in Autonomous Systems
- Optimization of Naval Platform Control Using AI: Adaptive Systems and Robust and Predictive Control Algorithms
- Advanced Simulation and Digital Twins for Naval mission training, planning, and evaluation
Integration of AI with naval command and control systems: interoperability, communication protocols, and scalable architectures
Development of ethical and regulatory frameworks for the responsible implementation of AI in the navy: legal, operational, and security considerations
- Project Introduction and Justification: Analysis of the current state of artificial intelligence in naval applications and definition of specific objectives oriented towards the autonomous monitoring and control of fleets
- Comprehensive Review of Artificial Intelligence Technologies: Supervised and Unsupervised Learning, Deep Learning, Convolutional and Recurrent Neural Networks, and their applicability to marine systems
- Integrated System Architecture: Modular design combining IoT sensors, edge computing, and satellite communication to ensure operability in adverse maritime environments
- Modeling and Simulation of Operational Scenarios: Creation of digital environments that allow for the validation of anomaly detection algorithms and real-time prediction of fleet behavior
- Development of Advanced Monitoring Algorithms: Implementation of machine learning models for recognizing navigation patterns, machinery wear, and predictive maintenance alerts
- Implementation of Autonomous Control Systems: Integration of adaptive and reinforcement control techniques for maneuvers Autonomous navigation, route optimization, and emergency response without human intervention.
Optimization of inter-naval communication: secure transmission protocols, low latency, and redundancy using mesh networks and satellites to guarantee the integrity and availability of critical data.
Incorporation of advanced cybersecurity: protection strategies against network attacks, sensor spoofing, and unauthorized access using artificial intelligence for dynamic detection and response.
Real-time system evaluation and validation: performance, reliability, scalability, and accuracy metrics through testing in simulated and real maritime environments.
Technical documentation and results presentation: preparation of detailed reports, user manuals, contingency plans, and a professional defense of the project before an expert committee in artificial intelligence and naval operations.
Career prospects
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- Marine Autonomous Systems Specialist: Development and management of autonomous navigation systems, underwater drones, and unmanned vehicles.
- Marine Data Analyst: Processing and analysis of large volumes of oceanographic, meteorological, and maritime traffic data for route and operational optimization.
- Naval Software Engineer: Development of software for control, communication, and data analysis systems in the maritime environment.
- Marine Industry Artificial Intelligence Consultant: Advising on the implementation of AI solutions for process optimization, safety, and energy efficiency.
- Naval AI Researcher: Development of new AI techniques and algorithms to solve specific problems in the maritime sector, such as threat detection, failure prediction, and optimization of Logistics.
- Maritime Cybersecurity Officer: Protecting critical systems and data against cyberattacks, using AI techniques for intrusion detection and prevention.
- Marine Predictive Model Developer: Creating predictive models for route optimization, weather forecasting, and risk analysis.
- Navigation Machine Vision Specialist: Developing machine vision systems for navigation assistance, obstacle detection, and object identification at sea.
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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
- AI in the Maritime Sector: Master the latest Artificial Intelligence techniques applied to route optimization, fleet management, and maritime safety.
- Marine Data Analysis: Learn to interpret and use large volumes of data for strategic decision-making in the maritime environment.
- Naval Automation and Robotics: Explore the applications of automation and robotics in improving the efficiency and safety of naval operations.
- Advanced Simulation and Modeling: Develop skills in simulation and modeling of maritime scenarios for resource optimization and risk prevention.
- Practical Projects and Case Studies Study Course: Apply your knowledge to real-world projects and analyze success stories in the implementation of AI in the maritime sector. Boost your career in the maritime sector with Artificial Intelligence tools.
Testimonials
I applied the knowledge from the Master’s in AI to the optimization of navigation routes for a fishing fleet, achieving a 15% fuel saving and an 8% increase in catch, minimizing the environmental impact and maximizing profitability.
During my Master’s degree in Electricity, Electronics and Communications, I developed an intelligent control system to optimize energy consumption in buildings, which demonstrated a 20% reduction in field tests and received the award for best final master’s project for its innovation and applicability.
I applied the knowledge from the Master in AI to the optimization of maritime routes for a fishing fleet, achieving a 15% increase in catch and a 12% reduction in fuel consumption in just six months.
During my Master’s degree in Artificial Intelligence Applied to the Marine, I developed a system for predicting optimal routes for vessels, taking into account real-time meteorological and oceanographic variables. This system, implemented in a case study with a fishing fleet, demonstrated a 15% increase in fuel efficiency and a 12% reduction in navigation time, exceeding the project’s initial expectations and generating significant interest from companies in the sector.
Frequently asked questions
Maritime sector.
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.
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.
- Project Introduction and Justification: Analysis of the current state of artificial intelligence in naval applications and definition of specific objectives oriented towards the autonomous monitoring and control of fleets
- Comprehensive Review of Artificial Intelligence Technologies: Supervised and Unsupervised Learning, Deep Learning, Convolutional and Recurrent Neural Networks, and their applicability to marine systems
- Integrated System Architecture: Modular design combining IoT sensors, edge computing, and satellite communication to ensure operability in adverse maritime environments
- Modeling and Simulation of Operational Scenarios: Creation of digital environments that allow for the validation of anomaly detection algorithms and real-time prediction of fleet behavior
- Development of Advanced Monitoring Algorithms: Implementation of machine learning models for recognizing navigation patterns, machinery wear, and predictive maintenance alerts
- Implementation of Autonomous Control Systems: Integration of adaptive and reinforcement control techniques for maneuvers Autonomous navigation, route optimization, and emergency response without human intervention.
Optimization of inter-naval communication: secure transmission protocols, low latency, and redundancy using mesh networks and satellites to guarantee the integrity and availability of critical data.
Incorporation of advanced cybersecurity: protection strategies against network attacks, sensor spoofing, and unauthorized access using artificial intelligence for dynamic detection and response.
Real-time system evaluation and validation: performance, reliability, scalability, and accuracy metrics through testing in simulated and real maritime environments.
Technical documentation and results presentation: preparation of detailed reports, user manuals, contingency plans, and a professional defense of the project before an expert committee in artificial intelligence and naval operations.
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.