AI-powered autonomy optimization course
About our
AI-Based Range Optimization Course
Explore the use of artificial intelligence (AI) to maximize efficiency and performance in various applications, from electric vehicles to renewable energy systems. It focuses on developing machine learning algorithms and optimization techniques to predict and improve battery life, energy management, and overall range. It includes big data analysis, scenario simulation, and the application of predictive models to minimize energy consumption and extend operating range.
This course provides tools and knowledge to implement AI solutions in systems design, route planning, and fleet management, thereby optimizing performance and sustainability. It addresses practical applications in sectors such as transportation, logistics, and energy, preparing participants for professional roles in resource optimization, energy data analysis, and AI software development—all aimed at boosting efficiency and reducing environmental impact.
AI-powered autonomy optimization course
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 3
699 $
Competencies and results
What you will learn
1. Optimizing Naval Autonomy with AI: Modeling and Performance of Propellers
- Master predictive modeling of propeller performance using artificial intelligence (AI).
- Apply AI optimization techniques for energy efficiency in naval propulsion systems.
- Understand and simulate the flow around propellers and complex propulsion systems.
- Use AI algorithms for cavitation prediction and mitigation.
- Implement AI models for managing endurance based on environmental and operational conditions.
- Analyze data from sensors and monitoring systems to optimize performance in real time.
- Develop AI-based predictive maintenance strategies to reduce costs and increase availability.
- Integrate AI into naval scenario simulations for strategic decision-making.
- Evaluate the impact of new technologies and materials on naval endurance.
- Create simulation models for evaluating energy consumption and autonomy scenarios.
2. Optimizing Naval Autonomy with AI: Propeller Design and Analysis
- Delve into the fundamentals of AI-optimized naval propeller design.
- Understand and apply numerical simulation techniques for propeller flow and performance analysis.
- Explore the use of AI algorithms for propeller shape optimization and drag reduction.
- Analyze the effects of cavitation and noise on propeller design.
- Evaluate the impact of propulsive efficiency on vessel range.
- Design and optimize propellers for different vessel types and operating conditions.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. Optimization of Naval Autonomy with AI: Evaluation and Efficiency of Propulsion Systems
4. Optimizing Naval Range with AI: Evaluation and Efficiency of Propulsion Systems
- Identify and understand the principles of Artificial Intelligence (AI) applied to the optimization of naval propulsion systems.
- Evaluate the performance of propulsion systems, including engines, propellers, and energy management systems, using AI algorithms.
- Analyze historical and real-time data to predict the behavior of propulsion systems and optimize their efficiency.
- Design and implement AI-based control strategies to reduce fuel consumption and emissions.
- Use simulation models and AI tools to evaluate the impact of different variables on the range and performance of vessels.
- Integrate AI systems with sensors and communication systems for the monitoring and remote control of propulsion systems.
- Explore the use of AI in route planning and
- Optimize speed to maximize range and minimize operating costs.
- Analyze case studies and practical examples of AI applications in naval range optimization.
- Understand the safety and ethical implications of AI in naval navigation and propulsion.
- Become familiar with the latest trends and developments in the field of AI and its application to the naval industry.
5. Optimizing Naval Autonomy with AI: Propulsion Analysis and Improvement
- Delve into the analysis of naval propulsion systems, evaluating their efficiency and optimization through the use of Artificial Intelligence (AI).
- Understand the fundamentals of AI and its specific application in improving ship autonomy, including machine learning and predictive analytics.
- Study naval propulsion modeling and simulation techniques, using advanced software tools to predict performance and optimize design.
- Evaluate different types of propulsion systems, such as propellers, diesel engines, and hybrid systems, analyzing their impact on autonomy and energy efficiency.
- Apply AI algorithms to optimize ship routes and speeds, considering factors such as weather conditions, fuel consumption, and safety.
- Analyze real-time data from propulsion systems, using AI techniques to identify patterns, detect faults, and predict maintenance.
- Develop strategies
- To explore the impact of AI on reducing polluting emissions and improving sustainability in the naval sector.
- To implement AI solutions for optimizing autonomy in different types of vessels, such as merchant ships, cruise ships, and warships.
- To study the latest trends and advances in the application of AI in naval propulsion, including research and development of new technologies.
6. Optimization of Naval Autonomy with AI: Analysis and Modeling of Rotors
- Identify and understand the critical factors that impact naval autonomy, including rotor design and efficiency.
- Apply Artificial Intelligence (AI) techniques for predictive analysis and optimization of propulsion systems.
- Study the modeling of naval rotors, including the simulation of their behavior under various operating conditions.
- Master the analysis of rotor dynamics, considering aspects such as flap-lag-torsion coupling, whirl flutter, and fatigue effects.
- Learn to dimension composite laminated structures, joints, and bonded joints using finite element analysis (FEA).
- Implement damage tolerance methodologies and non-destructive testing (NDT) techniques such as ultrasonic testing (UT), radiography (RT), and thermography for component evaluation.
Who this program is for:
AI-powered autonomy optimization course
- Engineers with degrees in Aerospace Engineering, Mechanical Engineering, Industrial Engineering, Automation Engineering, or related fields.
- Experts from OEM rotorcraft/eVTOL, MRO, consulting, and researchers from technology centers.
- Specialists in Flight Testing, certification, avionics, control, and flight dynamics interested in deepening their knowledge.
- Officials from regulatory bodies/authorities and professionals involved in the development of UAM/eVTOL who need to master aspects of regulatory compliance.
Suggested Requirements: Basic knowledge of aerodynamics, control systems, and structures is recommended. A B2+ or C1 level of English or Spanish is required. We offer bridging tracks to strengthen your existing knowledge.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
2.1 Introduction to Artificial Intelligence in Naval Propulsion
2.2 Fundamentals of Propeller Modeling
2.3 Data Collection and Preparation for AI
2.4 Selection and Application of Machine Learning Algorithms
2.5 Modeling Propeller Performance with AI
2.6 Validation and Verification of AI Models
2.7 Optimization of Propeller Design Using AI
2.8 Sensitivity Analysis and Scenario Simulation
2.9 Practical Implementation of AI Models in Software
2.10 Case Studies and Success Stories in the Naval Industry
3.1 Introduction to Propeller Design and Analysis
3.2 Fundamental Principles of Hydrodynamics
3.3 Modeling Propellers with AI: Key Parameters and Variables
3.4 Application of Machine Learning Algorithms for Design
3.5 Analyzing Propeller Performance Through Simulation
3.6 Propeller Design Optimization for Efficiency
3.7 Cavitation and Noise Considerations
3.8 Tools and Software for Propeller Design and Analysis
3.9 Case Studies and Practical Examples
3.10 Future Trends in Propeller Design with AI
4.1 Introduction to Rotor Simulation and Evaluation
4.2 Fundamentals of Computational Fluid Dynamics (CFD)
4.3 Rotor Modeling Using CFD and AI
4.4 Flow Simulation Around Rotors
4.5 Rotor Performance Evaluation: Thrust, Power, Efficiency
4.6 Rotor-Hull Interaction Analysis
4.7 Rotor Design Optimization Using Simulation and AI
4.8 Simulation Model Validation and Calibration
4.9 Software and Tools for Rotor Simulation 4.10 Case Studies and Practical Applications
5.1 Introduction to Propulsion System Evaluation and Efficiency
5.2 Key Components of Naval Propulsion Systems
5.3 Propulsion System Performance Evaluation
5.4 Energy Efficiency Optimization
5.5 Applying AI to System Evaluation and Improvement
5.6 Sensor and Telemetry Data Analysis
5.7 Propulsion System Modeling and Simulation
5.8 Predictive Maintenance Strategies
5.9 Case Studies and Efficiency Examples
5.10 The Future of Propulsion Systems with AI
6.1 Introduction to Naval Propulsion Analysis and Improvement
6.2 Review of Propulsion Principles
6.3 Propulsion Performance Data Analysis
6.4 Identifying Areas for Improvement 6.5 Application of AI for optimization.
6.6 Design of improvement strategies.
6.7 Simulation and evaluation of proposed improvements.
6.8 Implementation of changes and monitoring of results.
6.9 Case studies and practical examples.
6.10 Future trends in propulsion improvement with AI.
7.1 Introduction to rotor analysis and modeling.
7.2 Types of rotors and their characteristics.
7.3 Principles of rotor hydrodynamics.
7.4 Rotor modeling with AI.
7.5 Rotor performance analysis: thrust, torque, power.
7.6 Simulation of flow around rotors.
7.7 Optimization of rotor design with AI.
7.8 Modeling and analysis software and tools.
7.9 Case studies and practical applications. 7.10 Future Trends in Rotor Analysis and Modeling with AI
8.1 Introduction to the Study and Evaluation of Propeller Blades
8.2 Design and Manufacturing of Propeller Blades
8.3 Hydrodynamic Analysis of Blades
8.4 Performance and Efficiency Evaluation
8.5 Cavitation and Noise Considerations
8.6 Application of AI for Blade Optimization
8.7 Material Selection and Manufacturing Processes
8.8 Testing and Validation of Propeller Blades
8.9 Case Studies and Practical Examples
8.10 Future of Propeller Design and Evaluation with AI
9.1 Introduction to Rotor Design and Simulation
9.2 Design Principles for Naval Rotors
9.3 Rotor Simulation Methodologies
9.4 AI-Assisted Rotor Design
9.5 Rotor performance simulation.
9.6 Rotor design optimization.
9.7 Cavitation and noise considerations.
9.8 Model validation and verification.
9.9 Design and simulation software and tools.
9.10 Case studies and practical applications.
2.2 Introduction to Marine Propeller Design with AI
2.2 Fundamentals of Propeller Analysis: Theory and Numerical Methods
2.3 AI Modeling for Propeller Design
2.4 Optimization of Marine Propellers using Genetic Algorithms and AI
2.5 Flow Simulation around Propellers: CFD and AI
2.6 Propeller Performance Evaluation: Thrust, Torque, and Efficiency
2.7 Cavitation Analysis in Marine Propellers with AI
2.8 Propeller Design and Analysis for Different Types of Vessels
2.9 Integrating AI into the Propeller Design Process: Case Studies
2.20 Future Trends: AI and the Evolution of Marine Propeller Design
3.3 Introduction to Rotor Simulation: Fundamentals and Types of Naval Rotors
3.2 CFD Modeling for Rotors: Principles and Applications
3.3 Rotor Simulation with AI: Algorithms and Advanced Techniques
3.4 Rotor Performance Analysis: Efficiency, Thrust, and Torque
3.5 Cavitation Assessment in Rotors: Detection and Mitigation
3.6 Rotor Design Optimization with AI: Finding Optimal Solutions
3.7 Rotor Simulation under Different Operating Conditions: Waves and Currents
3.8 Rotor Life and Fatigue Analysis: Methods and Tools
3.9 Data Integration and Results Analysis: Visualization and Reporting
3.30 Case Studies: Real-World Applications and Practical Examples
4.4 Efficiency Concepts in Naval Propulsion Systems
4.2 Modeling Drag and Its Impact
4.3 Propeller Design and CFD Optimization
4.4 Material Selection and Its Influence on Efficiency
4.5 Artificial Intelligence in Performance Optimization
4.6 Energy Efficiency Analysis in Propulsion Systems
4.7 Efficiency Evaluation and Improvement Under Different Operating Conditions
4.8 Simulation and Analysis of Propulsion Systems with AI
4.9 Case Studies and Practical Applications
4.40 The Future of Naval Propulsion Optimization with AI
5.5 AI Fundamentals in Naval Autonomy
5.5 Propeller Modeling: Principles and Techniques
5.3 Introduction to Propeller Performance: Key Parameters
5.4 AI for Performance Optimization: Algorithms and Applications
5.5 Case Studies: Propeller Modeling and Performance
5.5 Introduction to Propeller Design: Geometry and Principles
5.5 Propeller Analysis with AI: Tools and Methods
5.3 Propeller Design Optimization: Key Aspects
5.4 Case Studies: Propeller Design and Analysis
5.5 Propeller Design Implementation and Evaluation
3.5 Introduction to Rotor Simulation: Methods and Techniques
3.5 AI in Rotor Simulation: Data Analysis
3.3 Rotor Performance Evaluation: Key Parameters
3.4 Case Studies: Rotor Simulation and Evaluation
3.5 Implementation of Rotor Simulation Rotors
4.5 Propulsion Systems: Components and Operation
4.5 AI for Efficiency Evaluation: Data Analysis
4.3 Optimizing Propulsion System Efficiency
4.4 Case Studies: System Evaluation and Efficiency
4.5 Implementing and Improving Efficiency
5.5 Naval Propulsion Analysis: Methods and Techniques
5.5 AI for Propulsion Improvement: Algorithms and Applications
5.3 Optimizing Propulsion Design
5.4 Case Studies: Propulsion Analysis and Improvement
5.5 Implementing and Evaluating Improvements
6.5 Rotor Analysis: Methods and Techniques
6.5 AI for Rotor Modeling: Tools and Methods
6.3 Optimizing Rotor Design
6.4 Case Studies: Rotor Analysis and Modeling
6.5 Implementing and Evaluating Modeling
7.5 Propulsion Blade Study: Geometry and Design
7.5 AI for Propeller Blade Evaluation: Methods and Techniques
7.3 Blade Design Optimization
7.4 Case Studies: Blade Study and Evaluation
7.5 Blade Design Implementation and Evaluation
8.5 Introduction to Rotor Design: Principles and Techniques
8.5 AI for Rotor Simulation: Tools and Methods
8.3 Rotor Design Optimization
8.4 Case Studies: Rotor Design and Simulation
8.5 Design Implementation and Evaluation
6.6 Introduction to Rotor Modeling: Concepts and Fundamentals
6.2 Rotor Design: Principles and Key Variables
6.3 Numerical Rotor Modeling: CFD and Finite Elements
6.4 Rotor Performance Analysis: Thrust, Power, and Efficiency
6.5 Environmental Influence on Rotor Modeling
6.6 Rotor Model Validation: Testing and Calibration
6.7 AI Applications in Rotor Modeling
6.8 Rotor Optimization with AI: Algorithms and Techniques
6.9 Case Studies: Rotor Modeling and Analysis in Practice
6.60 Future Trends in Rotor Modeling with AI
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees, and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
Do you have any questions?
Our team is ready to help you. Contact us, and we will respond as soon as possible.
F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).