Dr. Marina AlcoverAcademic director focused on measurable results and operational excellence in maritime education. Integrates IMO/STCW regulations and ISO 21001/9001 f
Masters · 1500 h · 11 modules
Master's in Artificial Intelligence Applied to 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…
Duration1500 h
FormatOnline
Starts14 December
Places20 / 20
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Programme overview
What is the Master’s in Artificial Intelligence Applied to the Navy?
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.
Programme type
Master's Degree
Study mode
Online
Duration
1,500 hours
Modules
11
Programme highlights
- 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.
Curriculum
Programme Structure
The programme is structured in 11 modules, listed below in order of study.
01
AI fundamentals, marine data and solutions architecture for operations in the maritime environment
- AI landscape applied to the naval domain: use cases, technical limitations and criteria of operational viability
- Typologies of marine data: telemetry, oceanography, AIS, meteo-mar, sonar and image, and its impact on modelling
- Data life cycle: capture, cleanup, labelling, balance and quality control with traceability
- AI architectures: Edge on board, on-premise on port and hybrid cloud with latency requirements
- Fundamentals of supervised/unsupervised learning and selection of algorithms by mission
- Performance metrics: Accuracy, Recall, F1, ROC-AUC, and risk-oriented operational metrics
- Experiment management: Data/model versioning, reproducibility and change control
- Risks of sea modelling: changing distribution, sensory noise and bias due to environmental conditions
- Technical Documentation: Data Sheets, Model Cards and Audit Assumption Report
- Integrator workshop: definition of naval use case with requirements, minimum data and acceptance KPI
02
Marine data engineering, sensory integration and automated collection pipelines
- Font Integration: AIS, VDR, ECDIS, Radar, IMU, CTD, ADCP and Machinery Logs
- Protocols and formats: NMEA, Modbus, MQTT, OPC UA, APIs and schema standardisation
- Temporary Synchronization: NTP/PTP, Time-Stamping, Drift and Multi-Sensor Coherence
- ETL/ELT in real time: parsing, enrichment, validation and error management
- Time Series Storage: Modelling, Partitioning, Retention and Compression
- Data Lake/Lakehouse for maritime operations: data government, lineage and access control
- Data quality in the field: Detection of outliers, gaps, calibrations and QA/QC Operating
- Pipeline Observability: Logs, Metrics, Traces, Latencies and SLA Alerts
- Data Security: Encryption, Segmentation, Hardening and Sea Service Continuity
- Labouratory: End-to-end pipeline design from sensor to dashboard with validation rules
03
Artificial Vision and Multisensor Perception for Navigation, Security and Maritime Surveillance
- Computer Vision Fundamentals: Detection, Segmentation, Tracking and Motion Estimation
- Detection of maritime objects: boats, buoys, obstacles and fauna in low visibility conditions
- Fusion Camera–Radar–AIS: Association of targets, resolution of ambiguity and consistency of state
- Image processing in extreme environments: mist, reflections, night, rain and vibration on board
- CNN/Transformer models for perception: precision trade-offs, latency and energy consumption
- Monitoring and tagging: Semi-automatic strategies, active learning and bias control
- Perception Assessment: MAP, IOU, Stage Robustness and Safety Metrics
- Edge deployment: Quantization, pruning, accelerators and performance profiles on shipped hardware
- Security and privacy: image processing, retention, anonymization and access policies
- Applied project: Detection and alert system with multi-sensor integration and technical report
04
AI for Oceanographic Prediction, Meteo-Sea and Operational Decision Making
- Key meteo-oceanic variables: wind, waves, currents, tides and their impact on the operation
- Time Series: Forecasting, Seasonality, Regime Changes and Window Validation
- Models for prediction: ARIMA/Prophet, recurring networks, temporary transformers and hybrid approaches
- Assimilation and fusion of sources: buoys, satellite, numerical models and sensors shipped
- Operational Risk Prediction: Go/No-Go Thresholds, Weather Windows and Safety Margin
- Route optimisation: cost, time, comfort, consumption and avoidance of adverse conditions
- Uncertainty and confidence: intervals, probabilistic calibration and decision under uncertainty
- Smart alarms: detection of oceanographic anomalies and operational triggers
- Integration with SOPS: Procedures, Scaling, Evidence and Registration of Decisions
- Case study: Mission planning with weather-sea prediction and acceptance criteria
05
AI for marine machinery, predictive maintenance and critical asset management
- Propulsion and auxiliary systems: instrumented variables, failure modes and criticality
- Condition Modelling: Vibration, Temperature, Pressure, Consumption and Degradation Signatures
- Anomaly detection: thresholds, statistical models, autoencoders and asset focus
- Failure Prediction: Remaining Useful Life (RUL), Survival and Maintenance-Based
- Integration with CMMS/EAM: Work orders, spare parts, SLAs and evidence closure
- AI-assisted root cause analysis: correlations, events, sequences and actions prioritisation
- Optimisation of stops: windows, resources, cost and minimisation of operational impact
- Validation in the plant and sea: tests, acceptance, false positives and iterative improvement
- Cybersecurity applied OT: segmentation, minimum access and immutable event log
- Workshop: Design of a predictive maintenance programme with KPIs and implementation plan
06
AI for maritime logistics, terminals, smart ports and supply chain
- Port and Terminal Processes: Arrival, Berth Planning, Yard, Gate and Hinterland
- ETA/ETD prediction and congestion: models, features, drift and season evaluation
- Optimisation of berths and resources: Assignment, sequencing and simulation of scenarios
- AI for yards and equipment: movements, availability, maintenance and reduction of re-manipulations
- Performance Analytics: Operating KPIs, Bottlenecks and Lean-type improvements
- Pricing and Revenue Management: Demand, elasticity, SLAs and penalties with data-driven approach
- Documentation and traceability - events, APIs/EDI, data quality and audit
- Digital Twin Conceptual: Twin Port/Terminal for Policy Test and Resilience
- Risks and continuity: contingencies, systemic failures and operational recovery plans
- Case study: Decision board with prediction and optimisation of terminal resources
07
Intelligent Marine Robotics: ROV/AUV, Autonomy, Mission and Control Planning
- Typologies of marine robots: ROV, AUV, USV and mission-oriented hybrid architectures
- Status estimation: IMU-DVL-pressure, relative navigation and controlled degradation
- Movement control: Advanced PID, thrust allocation, saturations and stability in 6 DOF
- Mission planning: waypoints, geofences, restrictions and abort criteria/safe return
- Underwater Perception: Sonar, Vision, Backscatter, Detection and Basic Classification
- Communications and Telemetry: Tether, Acoustics, Latency, Link Loss and Store-and-Forward Strategies
- SIL/HIL Simulation: Scenarios, Fault Injection and Performance Metrics
- Operational Security: Risk Matrixes, Procedures, Permits and Coordination with Support Ship
- Mission Data Management: Records, Synchronization, Evidence and Inspection Reports
- Applied project: design and validation of a robotized mission with complete technical delivery
08
Cybersecurity, functional security and resilience of AI systems in naval environment
- Attack surface in maritime systems: IT/OT, sensors, links and control stations
- Threats to models: data poisoning, adversarial, information leakage and input manipulation
- Security by design: Hardening, access control, segmentation, key rotation and logging
- Resilience and continuity: redundancies, failover, backups, recovery tests and safe degradation
- Functional security: limits, interlocks, watchdogs and safe stop modes in self-contained systems
- Incident management: detection, containment, forensic analysis and lessons learned
- Model Governance: Approvals, Changes, Audit, Traceability and Deployment Criteria
- Technical-Operative Compliance: Evidence, Records, Retention and Control of Suppliers
- Risk Assessment: Matrices, Thresholds, Prioritization and Acceptance for Criticality
- Workshop: Cybersecurity and resilience plan for an AI system shipped/ported
09
MMLOPS, Model Operation, Observability and Continuous Improvement in Maritime Production
- MMLOPS End-to-End: Development, Validation, Deployment and Operation with Version Control
- CI/CD for AI: Testing, Packaging, Data Validation and Quality Gates
- Model Monitoring: Drift, Performance Decay, Alerts and Governed Retraining
- Pipeline Observability: Latency, Availability, Errors, Backlog and SLA Compliance
- Experiment Management: Tracking, Comparisons, Reproducibility and Documentation
- Edge MOLPS: Onboard Deployment, Secure Updates, and Remote Management
- A/B Testing and Operational Assessment: Real Impact, Safety, Costs and Operator Experience
- Dataset Management and Continuous Labeling: Active Learning, Data Augmentation, and Bias Control
- Executive Reporting: KPIs, Risks, ROI and Backlog Prioritization of Improvements
- Labouratory: Implementation of a MLOPS cycle with controlled monitoring and retraining
10
Strategy, regulation, ethics and management of AI projects for the maritime industry
- Maritime Roadmap: Prioritization by Value, Risk, Complexity and Data Maturity
- Project Management: Scope, Schedule, Budget, Risks and Acceptance Criteria
- Procurement and suppliers: specifications, SLAs, data ownership/models and deliverables validation
- Ethics and responsibility: bias, explainability, transparency and automation limits
- Data Governance: Privacy, Retention, Access, Transfers and Internal Policies
- Change Management: Operational Adoption, Training, Roles and Organisational Resistance
- AI-assisted SOPS design: integration with operations, scaling and traceability
- Impact measurement: ROI, TCO, residual risk, security and business continuity
- Use case portfolio: Surveillance, Navigation, Maintenance, Logistics and Exploration
- Pre-TFM: Problem Definition, Methodology, Dataset, Metrics, and Validation Plan
11
Master’s Final Project in Artificial Intelligence Applied to the Navy
- Choice of topic and definition of the research problem
- Formulation of objectives, hypotheses and scope of work
- Review of the state of the art and specialised bibliography
- Analysis of the applicable regulatory and technical framework
- Design of the methodology and the work plan
- Data collection, processing and validation
- Technical development and analysis of results
- Discussion, conclusions and future lines of work
- Drafting of the memory in accordance with academic standards
- Presentation and public defence before the Court
Workload
Study Time and Workload
1500 hours of study, online and at your own pace. Move the slider to the hours a week you can really put in.
Hours a week8
Weeks to finish—
You finish around—
Who it is for
Naval and 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.
Faculty
Academic direction and faculty
The teaching team at Navalis: working professionals who deliver and oversee the institute's programmes.
Eng. Diego Cifuentes
Cap. Javier Abaroa (MCA)
Dr. Pau Ferrer
Dr. Nuria Llobregat
Eng. Javier Bañuls
Eng. Sofía Marquina
Eng. Tomás Riera
Verified reviews
Student Reviews
Reviews collected and verified by Opynio, not written by us.
Outcomes
Learning Outcomes
- 01Optimize resource management and naval logistics
- 02Develop systems for detecting and responding to maritime threats
- 03Design and simulate complex operational scenarios for strategic decision-making
- 04Automating the analysis of oceanographic data to improve the prediction of weather patterns
- 05Implement AI algorithms for optimizing navigation routes and fuel consumption
- 06Creating predictive models for the preventive maintenance of naval systems
Careers
Career Opportunities
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02Marine Autonomous Systems Specialist: Development and management of autonomous navigation systems, underwater drones, and unmanned vehicles.
03Marine Data Analyst: Processing and analysis of large volumes of oceanographic, meteorological, and maritime traffic data for route and operational optimization.
04Naval Software Engineer: Development of software for control, communication, and data analysis systems in the maritime environment.
05Marine Industry Artificial Intelligence Consultant: Advising on the implementation of AI solutions for process optimization, safety, and energy efficiency.
06Naval 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.
07Maritime Cybersecurity Officer: Protecting critical systems and data against cyberattacks, using AI techniques for intrusion detection and prevention.
08Marine Predictive Model Developer: Creating predictive models for route optimization, weather forecasting, and risk analysis.
09Navigation Machine Vision Specialist: Developing machine vision systems for navigation assistance, obstacle detection, and object identification at sea.
Questions
Entry Requirements
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.
Maritime sector.
Recommended functional SMCP. We offer support materials for standard phraseology.
Yes, with a relevant degree or experience in maritime/port operations. The admissions interview will confirm suitability.
Optional (3–6 months) through Companies & Collaborations and the Alumni Network.
Simulator practice (rubrics), defeat plans, SOPs, checklists, micro-tests and applied TFM.
A degree from Navalis Magna Institute + operational portfolio (tracks, SOPs, reports and KPIs) useful for audits and employment.
Online admissions
Admissions Process
No waiting for a phone call. If your documents are complete, you can pay in the same session.
Entry requirements
```htmlAcademic/Professional Profile:Bachelor's degree in Computer Science, Engineering, Mathematics, Physics, or a related field; or demonstrable professional experience in software development, data analysis, or related areas.Technical Skills:Fundamentals of programming (Python preferred), linear algebra, statistics, and probability. Basic knowledge of machine learning is valued.Documentation:Updated CV, copy of degree (if applicable), academic transcript (if applicable), cover letter highlighting interest in AI and its applications in the maritime sector.Technical
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Frequently Asked Questions
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