Master’s in Synthetic Data and Sensor Simulation
Online master's programme of 1,900 hours in synthetic data and sensor simulation: ray tracing, domain randomisation and scenario coverage. 12 modules covering synthetic data types, sensor modelling, rendering and virtual environments, perception simulators, generative models, randomisation and coverage, the sim-to-real gap, multimodal sensor simulation, validation, safety and regulation, and data production pipelines, with an assigned technical tutor.
Master’s in Synthetic Data and Sensor Simulation — studying from Australia
Data, artificial intelligence and simulation are central to Australian industries including mining, banking, health, agriculture and government. Organisations use machine learning, digital twins and advanced analytics to improve productivity, asset performance and decision making. National science agency CSIRO, along with universities and a growing start-up ecosystem, drives research and applied innovation. Australia has also developed national AI ethics principles, making responsible design an important professional consideration. Engineers in this area typically work for technology firms, consultancies, research organisations and large enterprises.
Fees for Australia: A$9,410 (AUD), with instalments and employer sponsorship available.
Relevant bodies in Australia
Typical roles
Questions from Australia
Which Australian bodies recognise data and AI engineering skills?
Engineers Australia and the Australian Computer Society (ACS) are the main professional bodies, and the ACS runs a skills assessment for migration purposes. SEIUM's private qualifications are not accredited by either. Whether a private qualification is valued is decided by the employer or the relevant body.
What data and AI careers exist in Australia for this training?
Australia has demand for data scientists, AI engineers and simulation analysts across finance, health, mining, government and technology firms. Employers mainly assess your portfolio, technical skills and results. A SEIUM qualification may support your profile, but each employer decides its relevance.
Can I study this online from Australia while working?
Yes, the programmes are fully online and taught in English, and no student visa is needed to study from home. Specialist courses are 300 hours, postgraduate diplomas 800 and master's 1,900, with an assigned technical tutor and a final project. 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
Model optical, LiDAR and radar sensors with physical realism.
Build parameterised virtual environments for data generation.
Apply generative models to augment and diversify datasets.
Design randomisation and scenario coverage strategies.
Reduce the gap between simulation and reality.
Validate the fidelity and usefulness of synthetic data.
Integrate synthetic generation into safety and type-approval processes.
Who it's for
- Synthetic data engineers.
- Sensor simulation engineers.
- Perception engineers in automotive and robotics.
- ADAS validation specialists.
- Simulation technical artists.
- Computer vision researchers.
- Engineering or science graduates looking to enter the automotive sector.
What you'll work with
- ISO 21448 (SOTIF) (safety of the intended functionality)
- ISO 26262 (road vehicle functional safety)
- ISO 34502 (scenario-based safety evaluation for automated driving)
- ASAM OpenSCENARIO (scenario description format)
- ASAM OpenDRIVE (road network description format)
- Regulation (EU) 2024/1689 (AI Act)
- ISO/PAS 8800 (AI safety in road vehicles)
- CARLA
- NVIDIA Isaac Sim
- Unreal Engine
- PyTorch
- ROS 2
12 modules
1.1Scarcity and bias in real data1.2Synthetic data versus augmentation1.3Tabular, image and signal synthetic data1.4Physical simulation versus generative models1.5The sim-to-real gap1.6Privacy and synthetic data1.7Success stories in autonomous driving1.8Cost and return of synthetic generation1.9Risks of overfitting to simulation1.10Case study: data needs of a perception system
2.1Cameras: optics, distortion and noise2.2LiDAR models and point clouds2.3Radar: echo, multipath and clutter2.4Inertial sensors and GNSS2.5Ultrasonic sensors2.6Sensor noise, bias and drift2.7Synchronisation and calibration2.8Ideal, probabilistic and physical models2.9Validation of sensor models2.10Lab: noise model of a camera
3.1Rasterisation and ray tracing3.2PBR materials and optical properties3.3Lighting, weather and time of day3.4Unreal Engine and Unity3.5Procedural scenes3.63D assets and scenario libraries3.7OpenDRIVE and OpenSCENARIO3.8Traffic and actor simulation3.9Performance and real-time simulation3.10Practical exercise: parameterised urban scenario
4.1CARLA for autonomous driving4.2NVIDIA Isaac Sim and Omniverse4.3Robot and factory simulation4.4Virtual sensors in Gazebo4.5Interfaces with ROS 24.6Automatic ground-truth labelling4.7Dataset formats and export4.8Scaling generation in the cloud4.9Scene reproducibility4.10Lab: labelled dataset with CARLA
5.1Variational autoencoders5.2Generative adversarial networks5.3Diffusion models5.4Text- and mask-conditioned generation5.5Domain translation with CycleGAN5.6NeRF and Gaussian splatting5.7Synthetic time-series generation5.8Tabular data with CTGAN5.9Quality control of generated data5.10Practical exercise: defect augmentation with diffusion
6.1Domain randomisation6.2Structured randomisation6.3Design of experiments for scenarios6.4Operational design domain (ODD) coverage6.5Edge cases and rare events6.6Importance sampling6.7Critical scenarios via adversarial search6.8Diversity metrics6.9Class balancing6.10Workshop: scenario coverage plan
7.1Measuring the domain gap7.2Unsupervised domain adaptation7.3Mixing real and synthetic data7.4Fine-tuning with little real data7.5Stylisation and photometric realism7.6Calibrating simulators with real data7.7Transfer in robotics7.8Sim and real cross-validation7.9Lessons from industrial projects7.10Lab: comparing models trained in sim and real
8.1Camera, LiDAR and radar fusion8.2Consistent simulation across sensors8.3Simulating sensor failures8.4Occlusions and adverse conditions8.5Physics-based radar simulation8.6Acoustic simulation8.7Tactile and force sensors8.8Sensor data for digital twins8.9Multimodal compute requirements8.10Practical exercise: synchronised multimodal dataset
9.1Fidelity metrics for synthetic data9.2FID, KID and perceptual metrics9.3Performance on downstream tasks9.4Statistical validation of distributions9.5Simulation credibility9.6Validation of simulation tools9.7Dataset traceability9.8Documentation with datasheets9.9Bias audits9.10Case study: validation report for a dataset
10.1SOTIF according to ISO 2144810.2ISO 26262 functional safety and simulation10.3Scenarios for ADS type approval10.4The AI Act and data governance10.5Privacy and anonymisation10.6Ownership and licensing of 3D assets10.7Risks of over-reliance on simulation10.8Documentation for certification bodies10.9Ethics of generated data10.10Case study: simulation-based safety argument
11.1Architecture of a data factory11.2Orchestration and generation queues11.3Versioning of scenes and datasets11.4Storage and catalogue11.5Integration with continuous training11.6GPU cost control11.7Managing data suppliers11.8Pipeline operational metrics11.9Failure-driven continuous improvement11.10Workshop: design of a synthetic data factory
12.1Definition of the perception problem12.2Operational domain analysis12.3Sensor and environment modelling12.4Dataset generation12.5Model training12.6Sim-to-real evaluation12.7Coverage and risk analysis12.8Conclusions and future work12.9Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
The programme is delivered 100% online 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 each intake, so it fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is CARLA, NVIDIA Isaac Sim, Unreal Engine, PyTorch and ROS 2, in free, academic or evaluation versions. Exercises are based on real procedures: generating labelled datasets in simulation, comparing models trained on real and synthetic data and critical scenario campaigns. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ISO 21448 (SOTIF), ISO 26262, ISO 34502, ASAM OpenSCENARIO, ASAM OpenDRIVE, Regulation (EU) 2024/1689, ISO/PAS 8800.
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
- 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.
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 CARLA, NVIDIA Isaac Sim, Unreal Engine, PyTorch and ROS 2, 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 suitable computer 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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