AI validation course in aeronautics
About our
The AI Validation in Aeronautics course focuses on the application of artificial intelligence (AI) and machine learning (ML) techniques to improve accuracy, efficiency, and safety in aircraft design, operation, and maintenance. It covers the validation of AI models for anomaly detection, predictive analytics, and aeronautical system optimization, integrating data processing and simulation tools with regulations such as EASA and the FAA. Use cases are explored in autonomous flight control, fault diagnosis, and predictive maintenance management. The course offers hands-on training in AI development environments, using aeronautical sensor data and simulations to validate algorithms and models. It addresses results interpretation, AI-related risk management, and safety regulation compliance. Participants will acquire skills for roles such as aeronautical data scientists, AI engineers for aviation, and model validation specialists, driving innovation in the aerospace industry.
AI validation course in aeronautics
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 3
275 $
Competencies and results
What you will learn
1. Validation of AI Systems for Aircraft: Comprehensive Learning
- Design and evaluate AI algorithms for autonomous flight control and navigation in aircraft.
- Understand the principles of Artificial Intelligence (AI) applied to aeronautical systems, including machine learning, neural networks, and natural language processing.
- Apply rigorous validation and verification methodologies to ensure the safety and reliability of AI systems in aircraft.
- Evaluate the performance of AI systems in simulated environments and under real flight conditions, using advanced analytical tools and techniques.
- Master international regulations and standards related to the certification and use of AI systems in the aerospace industry.
- Integrate AI systems with avionics electronics, sensors, and actuators to optimize aircraft performance and improve safety.
- Identify and mitigate the risks associated with the deployment of AI systems in aircraft, including the interpretation of results, transparency, and explainability.
Participate in the development and implementation of flight tests to evaluate the performance of AI systems under real-world conditions.
Learn about the latest trends in AI applied to aeronautics, such as predictive maintenance, air traffic management, and anomaly detection.
Develop skills for collaboration in multidisciplinary teams and effective communication of research and development results in the field of aeronautical AI.
2. Expert Domain in AI Validation for Aviation
- Comprehensive evaluation of aircraft structural integrity through the analysis of complex phenomena such as flap-lag-torsion, whirl flutter, and the impact of fatigue on critical components.
- Development of advanced skills in the design and dimensioning of aeronautical structures using composite materials, focusing on laminates, structural joints, and bonded joints, employing Finite Element Analysis (FEA) techniques for simulation and optimization.
- Practical application of damage tolerance methodologies and mastery of Non-Destructive Testing (NDT) techniques, including ultrasonic testing (UT), radiography (RT), and thermography, for the inspection, evaluation, and predictive maintenance of aircraft.
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. AI Validation in Aeronautics: A Practical Course
- Evaluate the robustness of AI models applied to aeronautics.
- Understand the challenges of validation in complex systems.
- Use AI-specific verification and validation techniques.
- Apply testing and performance evaluation methods.
- Interpret results and optimize AI models.
- Become familiar with industry tools and standards.
- Address ethical and security aspects in aeronautical AI.
5. Aeronautical AI: Validation of Systems and Applications
- Modeling and simulation of complex aeronautical systems using Artificial Intelligence (AI) techniques.
- Validation of AI algorithms for the analysis and prediction of aircraft behavior.
- Application of AI in the design and optimization of aeronautical structures, including weight reduction and efficiency improvement.
- Development of AI-based predictive models for early fault detection and maintenance management.
- Implementation of AI in flight control and navigation systems, optimizing safety and performance.
- Use of AI for flight data analysis and the identification of trends and patterns.
- Exploration of the latest AI trends for the aeronautical industry, including machine learning and natural language processing.
- Application of AI in aeronautical certification and compliance.
6. AI Validation in Aeronautics: Fundamentals and Applications
- Understand the fundamentals of Artificial Intelligence (AI) validation in the context of aeronautics.
- Identify and analyze the different types of AI used in aircraft design, manufacturing, and maintenance.
- Explore techniques and methodologies for validating the performance and safety of AI systems in aeronautical applications.
- Evaluate the challenges and opportunities that AI presents in the aerospace industry, including process automation, efficiency improvements, and design optimization.
- Study case studies and practical examples of AI implementation in aircraft, such as autonomous flight control systems, predictive fault detection, and flight data analysis.
Who this program is for:
AI validation course in aeronautics
- Graduates in Aerospace Engineering, Mechanical Engineering, Industrial Engineering, Automation Engineering, or related fields.
- Professionals in OEM rotorcraft/eVTOL, MRO, consulting, and technology centers.
- Flight Testing, certification, avionics, control, and dynamics seeking specialization.
- Regulators/authorities and UAM/eVTOL professionals requiring compliance skills.
Recommended requirements: foundation in aerodynamics and control and structures; ES/EN B2+/C1. We offer bridging tracks if you need them.
- 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.
Module 2 — Fundamentals of AI Validation in Aeronautics
2.1 Key AI Concepts: Machine learning, neural networks, deep learning.
2.2 Types of AI in aviation: Autonomous control systems, anomaly detection, predictive maintenance.
2.3 Validation and Verification (V&V) Processes: Methodologies and standards.
2.4 Data sources for AI: Acquisition, processing, and management.
2.5 Performance metrics: Accuracy, completeness, F1 score.
2.6 Biases and fairness in AI: Identification and mitigation.
2.7 Regulations and standards: EASA, FAA, RTCA.
2.8 Validation tools and technologies.
2.9 Safety and reliability considerations.
2.10 Case studies: Examples of AI validation in aviation.
2.2. Fundamentals of AI Algorithms in Aeronautics
2.2. Types of Algorithms: Selection and Application
2.3. Algorithm Design for AI Validation
2.4. Validation Methods: Testing, Simulation, and Analysis
2.5. AI Validation Tools and Platforms
2.6. Regulatory and Normative Aspects of Algorithm Validation
2.7. Techniques for Verifying AI Algorithms
2.8. Data Management and Quality for Validation
2.9. Case Studies: Real-World Applications and Challenges
2.20. The Future of AI Algorithms in Aeronautics
3.3 Introduction to Artificial Intelligence (AI) and its Foundations
3.2 Aeronautical Regulatory Framework: EASA, FAA, etc.
3.3 Specific Regulations for AI in Aeronautics
3.4 Functional Safety Standards: DO-378C, etc.
3.5 Cybersecurity in Aeronautical AI Systems
3.6 Ethics and Responsibility in AI Development
3.7 Introduction to Machine Learning
3.8 Introduction to Deep Learning
3.9 Data and Training of AI Models
3.30 The AI Systems Development Life Cycle
4.4 Introduction to Artificial Intelligence in Aeronautics
4.2 AI Principles: Machine Learning and Neural Networks
4.3 Aeronautical Regulatory Framework: EASA, FAA, and others
4.4 Certification Standards: DO-478C, DO-254, and their adaptation to AI
4.5 Ethical and Security Considerations in Aeronautical AI
4.6 Data Governance and Risk Management in AI Systems
4.7 Cybersecurity in AI Systems for Aircraft
4.8 Case Studies: AI Applications in Aviation
4.9 Tools and Technologies for AI System Development
4.40 Future Trends and Challenges of AI in Aeronautics
5.5 AI Systems Architecture for Aircraft: Introduction
5.5 AI Validation and Verification Methods
5.3 Testing and Evaluating AI Models
5.4 Safety Considerations in Aeronautical AI
5.5 Data and Training for AI in Aviation
5.6 Tools and Platforms for AI Validation
5.7 Case Studies: AI Applications in Aeronautics
5.8 Challenges and Future Trends in AI Validation
5.9 Regulatory Framework and Standards for AI in Aviation
5.50 Practical Implementation of AI Validation
6.6 Introduction to Aviation Legislation and AI Regulation
6.2 International and National Legal Framework for AI in Aviation
6.3 AI Systems in Aircraft: Types and Applications
6.4 AI Fundamentals: Applied Algorithms and Models
6.5 Architecture of AI Systems in Aviation
6.6 Design and Development of Safe and Reliable AI Systems
6.7 Ethical and Safety Considerations in Aeronautical AI
6.8 Industry Standards and Regulations for AI Systems
6.9 Introduction to the Validation and Verification of AI Systems
6.60 Challenges and Opportunities of AI in Aviation
- 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”).