Introduction to Marine AI Course
Why this course?
The Introduction to Marine AI
Immerse yourself in the future of navigation and maritime management. Discover how artificial intelligence is revolutionizing the sector, from route optimization and maintenance prediction to improved safety and process automation. This course provides you with a solid foundation for understanding and applying AI technologies in the marine environment, preparing you for the challenges and opportunities of tomorrow. You will learn about machine learning, computer vision, and natural language processing, and how they apply to practical cases in the naval industry.
Differential Advantages
- Real-world case studies: analysis of concrete examples of AI applications in navigation and port management.
- Tools and platforms: familiarization with the main AI tools and platforms used in the maritime sector.
- Industry experts: classes taught by professionals with experience in applying AI in the naval field.
- Project development: opportunity to develop practical projects to apply the knowledge acquired.
- Networking: contact with other professionals and companies in the sector interested in marine AI.
- Modality: Online
- Level: Cursos
- Hours: 150 H
- Start date: 25-07-2026
Availability: 1 in stock
Who is it aimed at?
- Naval engineers and marine architects looking to integrate AI into the design and optimization of vessels, exploring new frontiers in efficiency and safety.
- Merchant marine officers and fleet operators interested in leveraging AI for autonomous navigation, predictive maintenance, and optimized route management.
- Software developers and data scientists wanting to specialize in the development of AI algorithms for marine applications, from anomaly detection to fuel consumption optimization.
- Researchers and academics exploring the potential of AI for ocean research and marine environmental protection, including marine life monitoring and pollution detection.
- Students of engineering, marine science, and related fields seeking a competitive advantage in a rapidly evolving job market by acquiring cutting-edge marine technology skills.
Learning Flexibility:
Adapted for professionals and students: content accessible online 24/7, active discussion forums, and hands-on exercises with real-world applications.
Objectives and competencies
Optimize navigation and maritime safety:
Efficiently implement the travel plan (legs, waypoints), knowing the limitations of the nautical chart (scale, datum) and managing the information of navigational aids (lighthouses, buoys, beacons).
Automate the identification and tracking of marine objects:
“Integrating data from multiple sensors (radar, AIS, cameras) to create a unified and robust image, minimizing false positives and negatives.”
Implementing predictive maintenance systems on vessels:
“Using vibration analysis techniques, thermography, and oil analysis to anticipate failures and optimize maintenance planning.”
Improve operational efficiency and environmental sustainability:
Optimize ballast and fuel management, minimizing emissions and maximizing consumption efficiency.
Develop predictive models for fisheries resource management:
“Implement machine learning algorithms to predict biomass, distribution, and abundance of key species, considering environmental variables and historical fishing data.”
Design algorithms for the detection and mitigation of marine environmental risks:
Develop predictive models based on oceanographic, meteorological and maritime traffic data to identify areas of high risk of pollution or damage to sensitive ecosystems, implementing early warning protocols and alternative routes.
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 Sensors: Types, Characteristics, and Applications
- Fundamentals of Metrology: Precision, Accuracy, Resolution, and Calibration
- Temperature Sensors: Thermocouples, RTDs, Thermistors, and their Conditioning Circuits
- Pressure Sensors: Piezoelectric, Strain Gauges, Capacitive, and Industrial Applications
- Proximity and Position Sensors: Inductive, Capacitive, Ultrasonic, and Optical
- Vibration Sensors: Accelerometers, Velocity Meters, and Vibration Measurement Techniques
- Data Acquisition (DAQ) Systems: Architecture, Components, and Software
- Signal Processing: Filtering, Transforms Fourier and Spectral Analysis
- Introduction to Predictive Analytics: Concepts, Methodologies, and Benefits
- Predictive Maintenance: Strategies, Tools, and Case Studies
‘
- Introduction to AI for Autonomous Navigation: Basic Concepts and Applications
- Sensors for Autonomous Navigation: LiDAR, Radar, Cameras, IMU, and GNSS
- Environmental Perception: Image Processing, Sensor Fusion, and SLAM
- Route Planning and Obstacle Avoidance: Search Algorithms, A*, D*
- Control and Actuation: Motion Control, Predictive Control Algorithms
- Reinforcement Learning for Navigation: Q-Learning, Deep Q-Networks (DQN)
- Simulation and Validation: Simulation Environments, Real-World Testing
- Ethical and Safety Considerations in AI for Autonomous Navigation
- Regulations and Standards for autonomous navigation
- Future trends and challenges in AI and autonomous navigation
‘
- Introduction to sensors: types, characteristics, and applications
- Fundamentals of robotics: components, actuators, and control
- Predictive analytics: basic concepts, methods, and tools
- Data acquisition: sensor selection, interfaces, and protocols
- Signal processing: filtering, conditioning, and calibration
- Predictive modeling: regression, classification, and time series
- Collaborative robotics: safety, programming, and applications
- Predictive maintenance: monitoring, diagnostics, and prognosis
- Systems integration: architecture, communication, and deployment
- Case studies: applications in industry and the research
‘
- Introduction to Underwater Robotics: History, Applications, and Challenges
- Fundamentals of Underwater Mechanics: Hydrostatics, Hydrodynamics, and Buoyancy
- Underwater Sensors and Actuators: Cameras, Sonar, IMU, Motors, and Manipulators
- Underwater Communication Systems: Acoustics, Optics, and Tethering
- ROV and AUV Architectures: Design, Components, and Classification
- Underwater Motion and Navigation Control: PID, SLAM, and Visual Odometry
- Introduction to Artificial Intelligence: Concepts, Algorithms, and Applications
- Machine Learning for Underwater Robotics: Classification, Regression, and Clustering
- Underwater Machine Vision: Object Detection, Tracking, and 3D Reconstruction
- Ethics and Environmental Considerations
in underwater robotics
‘
- 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.
Plan de estudio - Módulos
- 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 Sensors: Types, Characteristics, and Applications
- Fundamentals of Metrology: Precision, Accuracy, Resolution, and Calibration
- Temperature Sensors: Thermocouples, RTDs, Thermistors, and their Conditioning Circuits
- Pressure Sensors: Piezoelectric, Strain Gauges, Capacitive, and Industrial Applications
- Proximity and Position Sensors: Inductive, Capacitive, Ultrasonic, and Optical
- Vibration Sensors: Accelerometers, Velocity Meters, and Vibration Measurement Techniques
- Data Acquisition (DAQ) Systems: Architecture, Components, and Software
- Signal Processing: Filtering, Transforms Fourier and Spectral Analysis
- Introduction to Predictive Analytics: Concepts, Methodologies, and Benefits
- Predictive Maintenance: Strategies, Tools, and Case Studies
‘
- Introduction to AI for Autonomous Navigation: Basic Concepts and Applications
- Sensors for Autonomous Navigation: LiDAR, Radar, Cameras, IMU, and GNSS
- Environmental Perception: Image Processing, Sensor Fusion, and SLAM
- Route Planning and Obstacle Avoidance: Search Algorithms, A*, D*
- Control and Actuation: Motion Control, Predictive Control Algorithms
- Reinforcement Learning for Navigation: Q-Learning, Deep Q-Networks (DQN)
- Simulation and Validation: Simulation Environments, Real-World Testing
- Ethical and Safety Considerations in AI for Autonomous Navigation
- Regulations and Standards for autonomous navigation
- Future trends and challenges in AI and autonomous navigation
‘
- Introduction to sensors: types, characteristics, and applications
- Fundamentals of robotics: components, actuators, and control
- Predictive analytics: basic concepts, methods, and tools
- Data acquisition: sensor selection, interfaces, and protocols
- Signal processing: filtering, conditioning, and calibration
- Predictive modeling: regression, classification, and time series
- Collaborative robotics: safety, programming, and applications
- Predictive maintenance: monitoring, diagnostics, and prognosis
- Systems integration: architecture, communication, and deployment
- Case studies: applications in industry and the research
‘
- Introduction to Underwater Robotics: History, Applications, and Challenges
- Fundamentals of Underwater Mechanics: Hydrostatics, Hydrodynamics, and Buoyancy
- Underwater Sensors and Actuators: Cameras, Sonar, IMU, Motors, and Manipulators
- Underwater Communication Systems: Acoustics, Optics, and Tethering
- ROV and AUV Architectures: Design, Components, and Classification
- Underwater Motion and Navigation Control: PID, SLAM, and Visual Odometry
- Introduction to Artificial Intelligence: Concepts, Algorithms, and Applications
- Machine Learning for Underwater Robotics: Classification, Regression, and Clustering
- Underwater Machine Vision: Object Detection, Tracking, and 3D Reconstruction
- Ethics and Environmental Considerations
in underwater robotics
‘
- 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 and Underwater Robotics: History and Evolution
- Underwater Sensors: Types, Operation, and Limitations
- Underwater Robotic Actuators: Hydraulic, Electric, and Pneumatic
- Underwater Communication: Acoustics, Optical, and Electromagnetic
- Underwater Navigation Systems: Inertial, Doppler, and Visual
- Underwater Environment Perception: Computer Vision, Sonar, and LiDAR
- Trajectory Planning and Control of Underwater Robots
- Machine Learning Applied to Underwater Robotics: Classification, Regression, and Clustering
- Applications of AI and Underwater Robotics: Inspection, Maintenance, and Repair
- Ethical and regulatory challenges of AI and underwater robotics
‘
- Introduction to marine sensors: types, applications, and challenges
- Fundamentals of oceanography: physical, chemical, and biological variables
- Data acquisition: sampling, calibration, errors, and validation
- Sensor networks: deployment, communication, power, and maintenance
- Data storage and management: databases, formats, and metadata
- Data preprocessing: cleaning, normalization, interpolation, and filtering
- Data visualization: tools, techniques, and graphical representation
- Introduction to machine learning: concepts, algorithms, and metrics
- Applications of machine learning: prediction, classification, and anomaly detection
- Ethics and privacy of marine data: legal and social considerations
‘
- Introduction to marine sensors: types, applications, and challenges.
- Fundamentals of data acquisition: sampling, resolution, and accuracy.
- Calibration and validation of sensors: methodologies and best practices.
- Sensors for measuring physical parameters: temperature, salinity, pressure, and currents.
- Sensors for measuring chemical parameters: dissolved oxygen, pH, nutrients, and pollutants.
- Sensors for measuring biological parameters: chlorophyll, biomass, and biodiversity.
- Data transmission and storage: protocols, formats, and platforms.
- Data quality control: anomaly detection, Error correction and imputation.
- Introduction to predictive modeling: types of models, selection, and evaluation.
- Applications of marine predictive models: coastal management, sustainable fisheries, and climate change.
‘
- Introduction to Naval Sensors: Types, Principles, and Applications
- Position and Motion Sensors: GNSS, IMU, Gyroscopes, Accelerometers
- Speed and Direction Sensors: Logs, Anemometers, Wind Vanes
- Depth and Distance Sensors: Echosounders, Sonar, LiDAR
- Environmental Sensors: Temperature, Pressure, Humidity, Salinity
- Communication and Telemetry Systems: Radio, Satellite, Fiber Optics
- Naval Automation: Propulsion, Steering, Loading, and Unloading Control
- Control Systems: Open Loop, Closed Loop, PID
- Systems Integration: Architectures, protocols, standards
- Maintenance and calibration of sensors and automated systems
‘
Career opportunities
- Software Developer for Autonomous Marine Systems: Design, programming, and testing of AI algorithms for vessel navigation and control.
- Marine Data Engineer: Collection, processing, and analysis of large volumes of oceanographic and maritime data to improve efficiency and safety at sea.
- Machine Vision Specialist for Marine Applications: Development of systems for detecting and recognizing objects in the water (vessels, obstacles, marine life) using AI.
- AI Consultant for the Maritime Industry: Advising shipping companies, ports, and other organizations on the implementation of AI solutions to optimize operations and reduce costs.
- Marine AI Researcher: Development of new AI algorithms and techniques to address specific challenges of the marine environment, such as current prediction, oil spill detection, and marine life monitoring. Marine.
- Marine AI Systems Maintenance Technician: Diagnosing and repairing AI equipment and software used in autonomous vessels and coastal monitoring systems.
- Marine AI Risk Analyst: Assessing and mitigating risks in maritime transport using AI-based predictive models.
- Marine AI Systems Cybersecurity Specialist: Protecting autonomous systems and maritime communication networks against cyberattacks.
“`
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.
5. Induction
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
- Marine AI Fundamentals: Master the key concepts and their application in the maritime sector.
- Case Studies: Learn with real-world examples how AI optimizes navigation, safety, and efficiency.
- Tools and Platforms: Discover the leading technologies for implementing AI solutions in marine environments.
- Marine Data Analysis: Acquire skills to interpret and utilize oceanographic and hydrographic data.
- Future of AI at Sea: Explore emerging trends and career opportunities in this innovative field.
Testimonials
This training provided me with a solid foundation in applying AI in the marine environment. I learned to use machine learning algorithms to analyze oceanographic data, identify species migration patterns, and predict harmful algal blooms—skills I successfully applied in my final project, where I developed a predictive model with 92% accuracy in the early detection of plastic pollution.
I mastered the programming of navigation and autonomous control systems, applying real-time route optimization algorithms that improved fuel efficiency by 12% during transatlantic navigation simulations.
The “Introduction to Marine AI” course provided me with the necessary foundation to develop an algorithm for detecting microplastics in underwater images. By applying the techniques I learned regarding convolutional neural networks and image processing, I achieved 95% accuracy in identifying different types of plastic, surpassing existing solutions and contributing to improved monitoring of ocean pollution. This project, initiated during the course, has received an award at an international conference and is currently being implemented in a marine research program.
I applied the knowledge from Introduction to Marine AI to develop an algorithm that predicts harmful algae blooms a week in advance, allowing local fish farms to take preventative measures and avoid losses of $200,000.
Frequently asked questions
Marine AI studies the application of artificial intelligence to problems and challenges related to the ocean.
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.
Prediction of optimal navigation routes to reduce fuel consumption and emissions.
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 Naval Sensors: Types, Principles, and Applications
- Position and Motion Sensors: GNSS, IMU, Gyroscopes, Accelerometers
- Speed and Direction Sensors: Logs, Anemometers, Wind Vanes
- Depth and Distance Sensors: Echosounders, Sonar, LiDAR
- Environmental Sensors: Temperature, Pressure, Humidity, Salinity
- Communication and Telemetry Systems: Radio, Satellite, Fiber Optics
- Naval Automation: Propulsion, Steering, Loading, and Unloading Control
- Control Systems: Open Loop, Closed Loop, PID
- Systems Integration: Architectures, protocols, standards
- Maintenance and calibration of sensors and automated systems
‘
Request information
- Complete the Application Form
- Attach your CV/Qualifications (if you have them to hand).
- Indicate your preferred cohort (January/May/September) and whether you want the hybrid option with simulator sessions.
Teachers
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.