Diploma in Artificial Intelligence for Naval Routes
Why this certificate program?
The Diploma in Artificial Intelligence for Naval Routes
This program prepares you to lead the digital transformation of the maritime sector. Master AI tools to optimize route planning, improve safety, and reduce environmental impact. Learn to implement predictive models, traffic control, and efficient resource management. This program provides you with the skills needed to innovate in the maritime navigation of the future.
Differential Advantages
- Practical Application of AI: Developing solutions for real-world navigation challenges.
- Route Optimization: Advanced algorithms to minimize costs and travel time.
- Predictive Analytics: Early risk identification and optimized decision-making.
- Maritime Safety: Improved safety through incident detection and prevention.
- Sustainability: Reduced environmental impact through optimized fuel consumption.
- Modality: Online
- Level: Diplomado
- Hours: 800 H
- Start date: 29-09-2026
Availability: 1 in stock
Who is it aimed at?
- Merchant Marine Officers and Captains who wish to optimize route planning, decision-making, and navigational safety.
- Naval Engineers and Navigation Systems Designers who seek to integrate AI into innovative solutions for maritime transport.
- Data Analysts and Software Developers interested in applying their skills in the maritime sector with a focus on AI.
- Shipping and maritime logistics companies seeking to implement AI solutions to improve operational efficiency and reduce costs.
- Researchers and academics who wish to deepen the study and application of artificial intelligence in the field of route planning Naval.
Study Flexibility:
Adapted to active professionals: content available online 24/7, discussion forums and personalized tutoring to answer questions.
Objectives and competencies
Optimizing the management of maritime resources:
“Planning safe and efficient journeys, considering environmental and operational factors, minimizing environmental impact and maximizing profitability.”
Implement efficient autonomous navigation systems:
Integrate data from multiple sensors (GPS, IMU, LIDAR, cameras) using Kalman filters or other sensor fusion algorithms for robust perception of the environment.
Predict and mitigate operational risks in real time:
“Implement predictive analytics based on historical data and current conditions (weather, traffic, equipment status) to anticipate failures and deviations, activating automated response protocols and alerting key personnel.”
Develop predictive models for preventive maintenance of vessels:
“Implement machine learning algorithms and historical data analysis to anticipate failures and optimize maintenance planning.”
Improving efficiency in logistics and maritime transport:
“Optimize route planning, considering factors such as currents, tides, weather conditions and port restrictions, to reduce fuel consumption and transit times.”
Automating maritime emergency detection and response:
Integrate sensor data (AIS, radar, cameras) to identify risk situations (collision, man overboard, fire) and activate predefined response protocols, optimizing coordination between rescue teams and competent authorities.
Curriculum - Modules
- Comprehensive Maritime Incident Management: protocols, roles, and chain of command for coordinated response
- Operational Planning and Execution: briefing, routes, weather windows, and go/no-go criteria
- Rapid Risk Assessment: criticality matrix, scene control, and decision-making under pressure
- Operational Communication: VHF/GMDSS, standardized reports, and inter-agency liaison
- Tactical Mobility and Safe Boarding: RHIB maneuvers, approach, mooring, and recovery
- Equipment and Technologies: PPE, signaling, satellite tracking, and field data logging
- Immediate Care of the Affected: primary assessment, hypothermia, trauma, and stabilization for evacuation
- Adverse Environmental Conditions: swell, Visibility, flows, and operational mitigation
Simulation and training: critical scenarios, use of VR/AR, and exercises with performance metrics
Documentation and continuous improvement: lessons learned, indicators (MTTA/MTTR), and SOP updates
- Introduction to Artificial Intelligence: basic concepts, types, and applications
- Fundamentals of Machine Learning: supervised, unsupervised, and reinforcement learning
- Natural Language Processing (NLP): text analysis, machine translation, and language generation
- Collection and preprocessing of maritime data: data sources, cleaning, and transformation
- Predictive modeling of ocean conditions: waves, currents, winds, and tides
- Route optimization: search algorithms, A*, Dijkstra’s algorithm, and heuristics
- Development of AI-based decision support systems
- Integration of AI with existing navigation systems: ECDIS, radar, and AIS
- Evaluation and validation of AI models in real maritime voyages
- Ethical and regulatory considerations in the application of AI in navigation
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- Introduction to AI in the Maritime Sector: Current Landscape and Future
- Machine Learning Fundamentals: Classification, Regression, and Clustering Algorithms
- Natural Language Processing (NLP): Sentiment Analysis, Chatbots, and Machine Translation
- Applied Computer Vision: Object Recognition, Satellite Image Analysis, and Surveillance
- Maritime Sensors and Data: Sensor Types, Acquisition, Preprocessing, and Analysis
- Predictive Modeling: Route Prediction, Consumption Optimization, and Predictive Maintenance
- Maritime Cybersecurity: Threats, Vulnerabilities, and Defense Strategies with AI
- AI Implementation in Navigation Systems: Route Optimization and Collision Avoidance
- Ethics and Responsibility in the Use of Maritime AI: Biases, Transparency, and Accountability
Case studies and practical applications: port optimization, supply chain security
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- Introduction to AI in the Maritime Environment: Challenges and Opportunities
- Machine Learning Fundamentals: Types of Algorithms, Supervised and Unsupervised Learning
- Natural Language Processing (NLP): Analysis of Maritime Texts, Risk Identification
- Computer Vision: Object Detection in Maritime Images and Videos (Vessels, Obstacles, etc.)
- Maritime Data Analysis: Time Series, Sensor Data, Historical Incident Records
- Predictive Modeling of Trails and Optimized Routes: Factors to Consider, Simulation
- AI for Maritime Risk Management: Pattern Identification, Threat Assessment
- AI-Based Early Warning Systems: Anomaly Detection, Notifications Automated
- AI Applications in Port Security: Access Control, Surveillance, Flow Analysis
- Ethical and Legal Considerations in the Use of AI in the Maritime Sector
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- Introduction to Predictive AI: Basic Concepts and Maritime Applications
- Machine Learning Fundamentals: Supervised and Unsupervised Learning
- Collection and Preprocessing of Maritime Data: Sources, Cleaning, and Transformation
- Predictive Modeling for Weather Conditions: Waves, Wind, and Currents
- Fuel Consumption Prediction: Speed and Route Optimization
- Maritime Risk and Safety Analysis: Anomaly and Pattern Detection
- Route Optimization and Voyage Planning: Algorithms and Constraints
- Integration of Predictive Models into Existing Navigation Systems
- Model Evaluation and Validation: Performance Metrics and adjustment
- Ethics and responsibility in the use of AI in maritime navigation
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- System Architecture and Components: Structural design, materials, and subsystems (mechanical, electrical, electronic, and fluid) with selection and assembly criteria for marine environments
- Fundamentals and Principles of Operation: Physical and engineering foundations (thermodynamics, fluid mechanics, electricity, control, and materials) that explain performance and operating limits
- Safety and Environmental (SHE): Risk analysis, PPE, LOTO, hazardous atmospheres, spill and waste management, and emergency response plans
- Applicable Regulations and Standards: IMO/ISO/IEC requirements and local regulations;
- Conformance criteria, certification, and best practices for operation and maintenance
- Inspection, testing, and diagnostics: Visual/dimensional inspection, functional testing, data analysis, and predictive techniques (vibration, thermography, fluid analysis) to identify root causes
- Preventive and predictive maintenance: Hourly/cycle/seasonal plans, lubrication, adjustments, calibrations, consumable replacement, post-service verification, and operational reliability
- Instrumentation, tools, and metrology: Measuring and testing equipment, diagnostic software, calibration and traceability; selection criteria, safe use, and storage
- Onboard integration and interfaces: Mechanical, electrical, fluid, and data compatibility; Sealing and watertightness, EMC/EMI, corrosion protection, and interoperability testing.
Quality, acceptance testing, and commissioning: process and materials control, FAT/SAT, bench and sea trials, go/no-go criteria, and evidence documentation.
Technical documentation and integrated practice: logs, checklists, reports, and a complete case study (safety → diagnosis → intervention → verification → report) applicable to any system.
- Introduction to AI in Maritime Transport: Benefits and Challenges.
- Machine Learning Fundamentals: Regression and Classification Algorithms.
- Collection and Preprocessing of Maritime Data: Sources, Cleaning, and Normalization.
- Analysis of Meteorological and Oceanographic Data: Wave, Wind, and Current Prediction.
- Vessel Performance Modeling: Fuel Consumption and Speed.
- Route Optimization Algorithms: A*, Dijkstra, Genetic Algorithms.
- Implementation of AI-Based Route Recommendation Systems.
- Safety and Risk Considerations in Route Optimization routes.
- Evaluation and validation of AI models for route optimization.
- Future trends and advanced applications of AI in maritime navigation.
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Career opportunities
- Nearby Officer / Route Analyst: Route optimization, condition prediction, navigational safety.
- Naval Systems Engineer (Development): AI integration into navigation systems, predictive maintenance.
- Navigation Software Developer: Creation of AI algorithms, advanced human-machine interfaces.
- Maritime Safety Researcher: Risk analysis, development of predictive accident models, improvement of safety protocols.
- Naval Operational Efficiency Consultant: Fuel consumption optimization, emissions reduction, strategic planning.
- Vessel Traffic Manager (VTS): Improvement of traffic monitoring and control, AI-assisted emergency response.
- Maritime Data Analyst: Data processing for decision-making, pattern and trend identification.
- Naval Cybersecurity Specialist: Protection of navigation systems against attacks, anomaly detection.
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Admission requirements
Academic/professional profile:
Degree/Bachelor's degree in Nautical Science/Maritime Transport, Naval/Marine Engineering, or a related field; or proven professional experience in bridge/operations.
Language proficiency:
Recommended functional maritime English (SMCP) for simulations and technical materials.
Documentation:
Updated resume, copy of degree or seaman's book, ID card/passport, letter of motivation.
Technical requirements (for online):
Equipment with camera/microphone, stable connection, ≥ 24” monitor recommended for ECDIS/Radar-ARPA.
Admission process and dates
1. Online
application
(form + documents).
2. Academic review and interview
(profile/objectives/schedule compatibility).
3. Admission decision
(+ scholarship proposal if applicable).
4. Reservation of place
(deposit) and registration.
5. Induction
(access to campus, calendars, simulator guides).
Scholarships and grants
- AI Fundamentals: Master the key algorithms for maritime route optimization and risk prediction.
- Predictive Analytics: Learn to anticipate weather conditions, maritime traffic, and fuel efficiency using AI models.
- Tools and Platforms: Familiarize yourself with industry-leading technologies, including autonomous navigation systems and simulation.
- Case Studies: Apply your knowledge in real-world simulations and case studies, optimizing routes and improving safety.
- Professional Certification: Earn a recognized diploma that will propel you to the forefront of intelligent and autonomous navigation.
Testimonials
This diploma program provided me with the necessary tools to optimize my company’s shipping routes. Thanks to the AI knowledge I gained, we were able to reduce transit times by 12% and fuel consumption by 8%, generating significant savings and greater efficiency in our operations.
The Advanced Navigation & Technology Diploma exceeded my expectations. I gained a solid foundation in digital cartography, optimized route planning, and the use of electronic instruments—skills I apply daily in my current role as a yacht captain. The hands-on training and focus on new technologies were key to my professional development.
I implemented the optimization algorithms learned in the diploma course to redesign the routes of our merchant fleet, achieving a 12% reduction in fuel costs and an 8% decrease in delivery times, exceeding the company’s expectations.
I implemented an AI system that optimized my company’s shipping routes, reducing transit times by 12% and fuel consumption by 8%, resulting in significant annual savings and a decrease in our carbon footprint.
Frequently asked questions
Maritime/naval and logistics 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.
Does this diploma program focus on optimizing naval routes using artificial intelligence techniques?
Yeah.
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.
- Introduction to AI in the Maritime Sector: Current Landscape and Trends
- Machine Learning Fundamentals: Classification, Regression, and Clustering Algorithms
- Natural Language Processing (NLP): Sentiment Analysis, Chatbots, and Virtual Assistants
- Computer Vision Applied to Navigation: Object Detection, Tracking, and Video Analysis
- Maritime Sensors and Oceanographic Data: Collection, Processing, and Analysis
- Predictive Modeling of Meteorological and Oceanic Conditions: Waves, Currents, and Winds
- AI for Maritime Route Optimization: Fuel Efficiency and Emission Reduction
- Anomaly Detection and Cybersecurity: Attack Prevention and Data Protection
- Predictive Maintenance of Onboard Equipment and Systems: Cost and Time Reduction inactivity
- Ethics and regulation of AI in the maritime industry: challenges and opportunities
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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.