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CourseDefense, Military Engineering & Land Systems Applications open

Course on the Impact of the AI Act on Military Land Vehicles

Online 300-hour course on the impact of the AI Act on military land vehicles. 6 modules covering Regulation (EU) 2024/1689 (AI Act), ISO/IEC 42001 (AI management systems) and Python (scikit-learn). Assigned tutor and enrolment open.

Duration300 h
ModeOnline
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Describe the structure and risk-based approach of the AI Act.

02

Delimit the actual scope of the Regulation, including the exclusion of exclusively military uses.

03

Analyse the obligations of high-risk systems in dual-use applications.

04

Evaluate data quality, bias and model validation.

05

Integrate AI governance requirements into procurement.

06

Assess the ethical aspects of AI in land vehicles.

07

Draft an integrative regulatory impact report.

Profile

Who it's for

  • AI regulation consultant wishing to specialise in Impact of the AI Act on military land vehicles.
  • Compliance manager in defence wishing to specialise in Impact of the AI Act on military land vehicles.
  • Responsible AI engineer wishing to specialise in Impact of the AI Act on military land vehicles.
  • Public procurement analyst wishing to specialise in Impact of the AI Act on military land vehicles.
  • Technology ethics adviser wishing to specialise in Impact of the AI Act on military land vehicles.
  • Land vehicle project manager wishing to specialise in Impact of the AI Act on military land vehicles.
  • Graduates in engineering or sciences seeking to enter the defence industry.
Standards & tools

What you'll work with

  • Regulation (EU) 2024/1689 (AI Act)
  • ISO/IEC 42001 (AI management systems)
  • ISO/IEC 23894 (AI risk management)
  • Directive 2009/81/EC (defence procurement)
  • ISO 21448 (SOTIF)
  • Regulation (EU) 2021/821 (dual-use items)
  • ISO/IEC 22989 (AI concepts and terminology)
Syllabus

6 modules · 300 h

  1. 1.1Purpose, scope and definitions
  2. 1.2Risk-based approach and its categories
  3. 1.3Prohibited practices
  4. 1.4High-risk systems and their annexes
  5. 1.5General-purpose AI models
  6. 1.6Exclusion of exclusively military uses
  7. 1.7Grey area: dual use and national security
  8. 1.8Application timeline
  9. 1.9Vocabulary and acronyms in the field
  10. 1.10Exercise: classifying several systems in a vehicle
  1. 2.1Perception and assisted driving systems
  2. 2.2Predictive maintenance and fleet management
  3. 2.3Operator decision-support systems
  4. 2.4Unmanned ground platforms
  5. 2.5Civil components and suppliers in dual-use chains
  6. 2.6Interaction with vehicle regulations
  7. 2.7Interaction with data protection
  8. 2.8Applicable product safety regulations
  9. 2.9Borderline cases and how to document them
  10. 2.10Practice: scope analysis of a programme
  1. 3.1Supplier quality management system
  2. 3.2Lifecycle risk management
  3. 3.3Data governance and dataset quality
  4. 3.4Required technical documentation
  5. 3.5Event logging and traceability
  6. 3.6Transparency and instructions for use
  7. 3.7Effective human oversight
  8. 3.8Accuracy, robustness and cybersecurity
  9. 3.9Conformity assessment and marking
  10. 3.10Practice: technical documentation index
  1. 4.1Representativeness of training data
  2. 4.2Bias and its detection in perception
  3. 4.3Validation under real and adverse conditions
  4. 4.4Performance metrics and their limits
  5. 4.5Validation of rare and edge cases
  6. 4.6Explainability and the limits of techniques
  7. 4.7Robustness testing against perturbations
  8. 4.8Performance monitoring in service
  9. 4.9Managing model drift
  10. 4.10Practice: validation plan for a perception system
  1. 5.1Allocation of responsibilities across the chain
  2. 5.2Contractual compliance clauses
  3. 5.3Requirements in public procurement tender specifications
  4. 5.4Relationship with the market surveillance authority
  5. 5.5Serious incidents and their reporting
  6. 5.6Debate on autonomy and human control
  7. 5.7Engineers' codes of professional ethics
  8. 5.8Public transparency and scrutiny
  9. 5.9Training of personnel involved
  10. 5.10Practice: tender specification with AI requirements
  1. 6.1Choice of vehicle and its AI systems
  2. 6.2Scope analysis and classification
  3. 6.3Risk management system
  4. 6.4Data governance plan
  5. 6.5Validation and monitoring plan
  6. 6.6Technical documentation and record-keeping
  7. 6.7Human oversight design
  8. 6.8Contractual compliance plan
  9. 6.9Dossier report
  10. 6.10Project defence
Methodology, software & labs

How you'll learn

Fully 100 % online training on the SEIUM campus, with downloadable material, recorded lectures and an assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, making it possible to combine study with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn), Fairlearn, MLflow, Jupyter, in free, academic or evaluation versions. Tests are studied using real procedures for regulatory case analysis, bias assessments on datasets and AI governance audits. Assessment is continuous, module by module, plus the final integrative project.

Accreditation-ready laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry-mentored:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:a safety-by-design approach, ciber-OT, DIH and compliance as pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Choice of vehicle and its AI systems

Describe the structure and risk-based approach of the AI Act.

Scope analysis and classification

Delimit the actual scope of the Regulation, including the exclusion of exclusively military uses.

Risk management system

Analyse the obligations of high-risk systems in dual-use applications.

Data governance plan

Evaluate data quality, bias and model validation.

Validation and monitoring plan

Integrate AI governance requirements into procurement.

Career opportunities

Where this takes you

AI regulation consultantCompliance manager in defenceResponsible AI engineerPublic procurement analystTechnology ethics adviserLand vehicle project manager
Admissions & scholarships

Requirements

  • No prior degree required.
  • Internet connection and a computer capable of running the programme's software.
  • Spanish language level sufficient to follow technical regulatory texts.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme consists of 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.

The course uses Python (scikit-learn), Fairlearn, MLflow, Jupyter. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

SEIUM's own diploma upon passing the continuous assessment and the integrative project. It is the Institute's own training, not an official qualification from the Ministry.

Yes. Modules passed in a course or postgraduate diploma in the same area can be recognised towards the advanced programme. Check the credit recognition policy when you enrol.

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