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Master'sDefense, Military Engineering & Land Systems Applications open

Master’s in Autonomy, Responsible AI & Software Engineering

Online master's programme of 1,900 hours in autonomy, responsible AI and software engineering, structured in 12 modules covering software for autonomy, applied machine learning, responsible AI principles, explainability, model robustness, verification, safety-critical software certification, DevSecOps and MLOps and the regulatory framework, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Design software architectures for autonomous systems.

02

Apply machine learning techniques to perception and decision-making.

03

Integrate responsible AI principles into the life cycle.

04

Assess the explainability and robustness of models.

05

Verify safety-critical software according to sector standards.

06

Implement DevSecOps and MLOps practices.

07

Analyse compliance with the EU AI Act.

Profile

Who it's for

  • Autonomous systems software engineers in automotive, robotics or aerospace.
  • Machine learning engineers building perception or decision models.
  • Responsible AI specialists implementing governance and auditing.
  • Software verification engineers moving into AI-based systems.
  • MLOps engineers deploying and monitoring models in production.
  • AI compliance consultants working with the AI Act and ISO/IEC 42001.
  • Engineering or science graduates aiming to enter the automotive and mobility sector.
Standards & tools

What you'll work with

  • Regulation (EU) 2024/1689 (Artificial Intelligence Act)
  • ISO/IEC 42001 (AI management systems)
  • RTCA DO-178C (software considerations in airborne systems)
  • ISO/IEC 23894 (AI risk management)
  • UL 4600 (safety of autonomous products)
  • IEC 61508-3 (functional safety, software requirements)
  • IEEE 7000 (addressing ethical concerns during system design)
  • MISRA C:2023 (C coding guidelines for critical systems)
  • ROS 2
  • PyTorch
  • MLflow
  • Adversarial Robustness Toolbox
  • SHAP
Syllabus

12 modules · 1900 h

  1. 1.1Software architectures for autonomous systems
  2. 1.2Sense-plan-act architecture
  3. 1.3Layered and deliberative architectures
  4. 1.4ROS 2 and DDS middleware
  5. 1.5Real-time systems and scheduling
  6. 1.6Safety-critical software life cycle
  7. 1.7Software requirements derived from the system
  8. 1.8Software quality according to ISO/IEC 25010
  9. 1.9Responsibility of the software engineer
  10. 1.10Case study: software architecture of a service robot
  1. 2.1Supervised and unsupervised learning
  2. 2.2Deep neural networks
  3. 2.3Computer vision with CNNs and transformers
  4. 2.4Reinforcement learning
  5. 2.5Imitation learning
  6. 2.6Training and regularisation
  7. 2.7Datasets and annotation
  8. 2.8Evaluation and metrics
  9. 2.9Deployment on embedded hardware
  10. 2.10Lab: training a classifier with PyTorch
  1. 3.1OECD AI Principles
  2. 3.2EU Ethics Guidelines for Trustworthy AI
  3. 3.3Principles of responsible use in defence
  4. 3.4Fairness, transparency and accountability
  5. 3.5Privacy by design
  6. 3.6AI sustainability
  7. 3.7Human control and oversight
  8. 3.8Value-based design with IEEE 7000
  9. 3.9AI governance within the organisation
  10. 3.10Workshop: ALTAI assessment of a system
  1. 4.1Interpretable-by-design models
  2. 4.2Post hoc methods: LIME and SHAP
  3. 4.3Saliency maps and Grad-CAM
  4. 4.4Counterfactual explanations
  5. 4.5Explainability in reinforcement learning
  6. 4.6Evaluating explanations
  7. 4.7Explanations for different users
  8. 4.8Limitations and risks
  9. 4.9Explainability tools
  10. 4.10Practical exercise: explaining a detector with SHAP and Grad-CAM
  1. 5.1Adversarial examples and evasion attacks
  2. 5.2Data poisoning
  3. 5.3Certified robustness
  4. 5.4Out-of-distribution data detection
  5. 5.5Uncertainty quantification
  6. 5.6Model calibration
  7. 5.7Cybersecurity of AI systems
  8. 5.8OWASP Machine Learning Top 10
  9. 5.9Model stress testing
  10. 5.10Lab: adversarial robustness with IBM's ART
  1. 6.1Formal verification of neural networks
  2. 6.2Model checking of behaviours
  3. 6.3Scenario-based testing
  4. 6.4Test case generation
  5. 6.5Simulation for verification
  6. 6.6Runtime monitoring
  7. 6.7Coverage in AI-based systems
  8. 6.8AMLAS assurance arguments
  9. 6.9UL 4600 standard
  10. 6.10Practical exercise: property verification with Marabou
  1. 7.1DO-178C and DAL levels
  2. 7.2IEC 61508 Part 3
  3. 7.3ISO 26262 Part 6
  4. 7.4EN 50716 for railways
  5. 7.5MISRA C and CERT coding standards
  6. 7.6Static and dynamic analysis
  7. 7.7MC/DC coverage
  8. 7.8Qualified tools
  9. 7.9EASA guidance on machine learning
  10. 7.10Workshop: static analysis with Polyspace or Cppcheck
  1. 8.1Continuous integration and deployment
  2. 8.2Security in the development pipeline
  3. 8.3Dependency management and SBOM
  4. 8.4MLOps: data and model versioning
  5. 8.5Monitoring models in production
  6. 8.6Data drift and retraining
  7. 8.7Infrastructure as code
  8. 8.8Artefact traceability
  9. 8.9Life-cycle auditing
  10. 8.10Practical exercise: MLOps pipeline with MLflow and GitLab CI
  1. 9.1Sheridan's levels of automation
  2. 9.2Adjustable autonomy
  3. 9.3Calibrated trust
  4. 9.4Operator situational awareness
  5. 9.5Design of supervisory interfaces
  6. 9.6Control transitions
  7. 9.7Human factors in verification
  8. 9.8Operator training
  9. 9.9Experimental evaluation with users
  10. 9.10Case study: design of a supervisory interface
  1. 10.1EU AI Act, Regulation (EU) 2024/1689
  2. 10.2High-risk systems and obligations
  3. 10.3Harmonised standards under development
  4. 10.4ISO/IEC 42001 AI management system
  5. 10.5ISO/IEC 23894 risk management
  6. 10.6Liability for defective products
  7. 10.7International humanitarian law and autonomy
  8. 10.8Legal review of new technologies
  9. 10.9Data protection
  10. 10.10Workshop: compliance analysis against the AI Act
  1. 11.1Autonomous road vehicles
  2. 11.2Collaborative industrial robots
  3. 11.3Inspection drones
  4. 11.4Decision support systems
  5. 11.5Healthcare robotics
  6. 11.6Maritime autonomous systems
  7. 11.7Lessons from incidents involving autonomy
  8. 11.8Transfer of good practice
  9. 11.9Research trends
  10. 11.10Case study: analysis of an autonomous driving incident
  1. 12.1Definition of the autonomous system
  2. 12.2Requirements and risk analysis
  3. 12.3Software architecture
  4. 12.4Development of the AI component
  5. 12.5Robustness and explainability assessment
  6. 12.6Verification plan
  7. 12.7Regulatory and ethical analysis
  8. 12.8Conclusions and future work
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so you can combine it with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is ROS 2, PyTorch, MLflow, Adversarial Robustness Toolbox and SHAP, in free, academic or evaluation versions. Testing is studied through real procedures for adversarial robustness evaluation of models, verification of safety-critical software through static analysis and scenario-based autonomy testing. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Regulation (EU) 2024/1689, ISO/IEC 42001, RTCA DO-178C, ISO/IEC 23894, UL 4600, IEC 61508-3, IEEE 7000, MISRA C:2023.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Regulation (EU) 2024/1689 on Artificial Intelligence, ISO/IEC 42001 and RTCA DO-178C applied to real cases, not as a theoretical annex.
Industry software:ROS 2, PyTorch, MLflow, Adversarial Robustness Toolbox and SHAP, in free, academic or evaluation versions.
Master's Thesis with defence:A responsible autonomous system with its AI component, robustness and explainability assessment, verification plan and regulatory analysis, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

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

System and risk definition
Define the autonomous system and analyse its requirements and risks.
Software architecture
Design the ROS 2 software architecture of the system.
AI component development
Develop and train the AI component with PyTorch and track it with MLflow.
Robustness and verification
Assess robustness and explainability and draw up the verification plan.
Master's Thesis
Present the system with its regulatory and ethical analysis in the thesis, defended before a panel.
Career opportunities

Where this takes you

Autonomous Systems Software EngineerMachine Learning EngineerResponsible AI SpecialistSoftware Verification EngineerMLOps EngineerAI Compliance Consultant
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

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 totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with ROS 2, PyTorch, MLflow, Adversarial Robustness Toolbox and SHAP, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme software, and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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