Master’s in AI Governance & EU AI Act Engineering for Mobility Products
Online master's programme of 1,900 hours in AI governance and EU AI Act engineering for mobility products. 12 modules covering AI in mobility, the EU AI Act, high-risk system requirements, ISO/IEC 42001, AI safety in vehicles, data bias, validation and explainability, conformity assessment, cybersecurity and corporate governance, with an assigned technical tutor.
Master’s in AI Governance & EU AI Act Engineering for Mobility Products — studying from Australia
Technology-driven organisations in Australia need professionals who combine engineering knowledge with business management, project governance and policy understanding. Sectors such as mining, energy, infrastructure and defence rely on technical managers who can translate complex engineering decisions into investment and regulatory terms. Government departments, industry bodies and consultancies in Canberra, Sydney and Melbourne actively shape technology policy, including innovation, digital transformation and critical technologies. Graduates often move into programme management, consulting or strategy roles within large engineering-intensive organisations.
Fees for Australia: A$15,800 (AUD), with instalments and employer sponsorship available.
Relevant bodies in Australia
Typical roles
Questions from Australia
Which Australian bodies relate to engineering management and technology policy?
Engineers Australia is the main professional body and offers pathways for engineering managers, while the Australian Institute of Company Directors and CPA Australia cover management and governance. SEIUM's private qualifications are not accredited by any of these bodies. Recognition is decided by employers or the relevant body, so verify it before enrolling.
What roles in Australia could this qualification support?
Engineering management, technology policy, and project leadership roles exist across government, utilities, consultancies and industry. Employers generally weigh your experience and leadership record heavily. A SEIUM qualification may add to your profile, but its acceptance depends on each employer.
Can I study this from Australia without a visa and with a job?
Yes, the programmes are fully online and in English, so you can study from Australia with no student visa required. Plan around 300 to 1,900 hours depending on the programme, and note an assigned technical tutor and a final project are included. Fees can be paid in instalments, and employer sponsorship may be possible.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Interpret the obligations of the EU AI Act as applied to mobility products.
Classify AI systems according to their risk level.
Implement an AI management system in line with ISO/IEC 42001.
Integrate functional safety and SOTIF with AI requirements.
Assess data quality, bias and model robustness.
Plan conformity assessment and post-market monitoring.
Establish governance structures and corporate accountability for AI.
Who it's for
- AI governance leads setting up policies, registers and committees.
- Safety engineers working on AI-based perception and driver assistance systems.
- Regulatory compliance consultants preparing clients for the EU AI Act.
- AI system auditors who need a solid technical and regulatory grounding.
- Type-approval managers for intelligent vehicle systems.
- Technology ethics officers in automotive and mobility companies.
- Engineering or science graduates seeking to enter the automotive sector.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- ISO/IEC 42001:2023 (AI management system)
- ISO/PAS 8800:2024 (road vehicles, safety and artificial intelligence)
- ISO/IEC 23894:2023 (AI risk management)
- ISO 21448:2022 (safety of the intended functionality, SOTIF)
- ISO 26262:2018 (road vehicles functional safety)
- Regulation (EU) 2019/2144 (general safety of vehicles)
- Directive (EU) 2024/2853 (liability for defective products)
- MLflow
- IBM AI Fairness 360
- SHAP
- CARLA
- Jira
12 modules · 1900 h
1.1Machine learning and deep learning1.2Perception with computer vision1.3Sensor fusion and planning1.4In-cabin language models1.5ADAS and automated driving systems1.6AI-based predictive maintenance1.7Life cycle of an embedded model1.8AI limitations and failure modes1.9SAE J3016 taxonomy1.10Case study: inventory of AI functions in a vehicle
2.1Structure of Regulation (EU) 2024/16892.2Prohibited AI practices2.3Classification of high-risk systems2.4Relationship with product harmonisation legislation2.5Obligations of providers and deployers2.6General-purpose AI models2.7Transparency and interaction with people2.8Application timeline2.9Market surveillance authorities2.10Practical exercise: risk classification of five mobility systems
3.1Risk management system3.2Data governance and quality3.3Annex IV technical documentation3.4Automatic event logging3.5Transparency and instructions for use3.6Human oversight measures3.7Accuracy, robustness and cybersecurity3.8Quality management system3.9Documentation retention3.10Workshop: Annex IV technical documentation template
4.1The ISO/IEC 42001 standard4.2Context and scope of the system4.3AI policies and roles4.4AI system impact assessment4.5Risk management according to ISO/IEC 238944.6Annex A controls4.7Internal audit of the AI management system4.8Integration with ISO 9001 and IATF 169494.9The NIST AI RMF framework4.10Case study: management system at a Tier 1 supplier
5.1ISO 26262 and learning-enabled components5.2SOTIF according to ISO 214485.3ISO/PAS 8800 on AI in vehicles5.4Critical scenarios and triggering conditions5.5Operational design domain coverage5.6Safety arguments with GSN5.7In-service monitoring5.8UN R157 and lane keeping systems5.9Regulation (EU) 2022/1426 on automated systems5.10Practical exercise: safety argument for a pedestrian detector
6.1Quality and representativeness of datasets6.2Demographic bias in perception6.3Labelling and annotation control6.4Synthetic data and simulation6.5Fairness metrics6.6Traceability of data lineage6.7Protection of personal data6.8Biometric data in driver monitoring6.9Copyright in training data6.10Lab: bias audit of an image dataset
7.1Model testing strategies7.2Robustness and adversarial testing7.3Uncertainty and calibration7.4Explainability with SHAP and saliency maps7.5Simulation and test-track testing7.6Quantitative acceptance criteria7.7Model version management7.8Retraining and substantial modifications7.9Test records7.10Lab: robustness analysis of a classifier
8.1Internal control procedures8.2Role of notified bodies8.3Harmonised standards under development8.4CE marking and EU declaration of conformity8.5Interaction with type approval8.6Regulation (EU) 2019/2144 on general safety8.7Registration in the EU database8.8Post-market monitoring plan8.9Reporting serious incidents8.10Case study: conformity route for a driver assistance system
9.1Threats specific to AI models9.2Data poisoning9.3Evasion and adversarial examples9.4UN R155 requirements applied to AI9.5OTA model updates and UN R1569.6Protecting model ownership9.7Fleet monitoring9.8Vulnerability response9.9The Cyber Resilience Act9.10Practical exercise: threat modelling of a perception system
10.1The Product Liability Directive10.2Civil liability in automated driving10.3Ethical principles of trustworthy AI10.4Ethics and AI committees10.5Board oversight10.6Contracts with AI suppliers10.7Insurance and risk transfer10.8Communication with users10.9Managing reputational risk10.10Case study: ethical decision after a field failure
11.1Organisational gap analysis11.2Inventory and register of AI systems11.3Roles of the AI officer11.4AI training and literacy11.5AI governance tools11.6Compliance indicators11.7Third-party audits11.8Compliance roadmap11.9Coordination with other regulations11.10Workshop: 24-month compliance plan
12.1Selecting the target AI system12.2Regulatory classification of the system12.3Risk analysis of the AI system12.4Designing the governance framework12.5Proposed technical documentation12.6Validation strategy12.7Post-market monitoring plan12.8Conclusions and recommendations12.9Master's Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
The programme is delivered 100% online on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so you can combine it with your job.
Each module combines theory, guided reading of the original standards and applied exercises. The working software is MLflow, IBM AI Fairness 360, SHAP, CARLA and Jira, in free, academic or evaluation versions. Exercises are based on real procedures: risk classification of a vehicle's AI functions, preparing technical documentation for a high-risk system and auditing the bias and robustness of a perception model. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: Regulation (EU) 2024/1689 (AI Act), ISO/IEC 42001:2023, ISO/PAS 8800:2024, ISO/IEC 23894:2023, ISO 21448:2022 (SOTIF), ISO 26262:2018, Regulation (EU) 2019/2144 and Directive (EU) 2024/2853.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- An internet connection and a computer capable of running the programme's 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.
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 MLflow, IBM AI Fairness 360, SHAP, CARLA and Jira, 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 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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