Master'sData, AI, Simulation & Analytics Applications open

Master’s in Verification, Validation and UQ of Models

Online master's programme of 1,900 hours in verification, validation and uncertainty quantification of models: uncertainty, V&V, safety cases and confidence. 12 modules covering model credibility, code and solution verification, experimental validation, uncertainty quantification, sensitivity analysis, Bayesian calibration, surrogate models and credibility standards, with ASME V&V 10, ASME V&V 20 and ASME V&V 40, and an assigned technical tutor.

Duration12–14 months

ModeOnline

LanguageEnglish

Start datesSet by the faculty

AwardSEIUM Institute diploma

Enrol now View syllabus Ask an advisor

Master’s in Verification, Validation and UQ of Models — 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$14,200 (AUD), with instalments and employer sponsorship available.

Relevant bodies in Australia

Australian Computer Society (ACS)
CSIRO
Department of Industry, Science and Resources
Standards Australia

Typical roles

Data Scientist
Machine Learning Engineer
Simulation Engineer
Analytics Engineer

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.

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Learning outcomes

What you will be able to do

Establish credibility plans according to the context of use.

Verify codes and numerical solutions.

Design and analyse validation experiments.

Propagate uncertainties through computational models.

Apply global sensitivity analysis.

Calibrate models with Bayesian methods.

Apply V&V standards in regulated sectors.

Profile

Who it's for

Verification and validation engineers.
Uncertainty quantification specialists.
Simulation engineers.
Model credibility analysts.
Computational reliability engineers.
Computational science researchers.
Engineering or science graduates looking to move into the aerospace sector.

Standards & tools

What you'll work with

ASME V&V 10 (verification and validation in computational solid mechanics)
ASME V&V 20 (verification and validation in CFD and heat transfer)
ASME V&V 40 (credibility of computational models for medical devices)
NASA-STD-7009 (standard for models and simulations)
ISO/IEC Guide 98-3 (GUM, guide to the expression of uncertainty in measurement)
JCGM 101 (propagation of distributions using a Monte Carlo method)
AIAA G-077 (guide for V&V of CFD simulations)
Dakota
UQLab
SALib
PyMC
OpenTURNS

Syllabus

12 modules

1.1Models in decision-making
1.2Verification versus validation
1.3The life cycle of a model
1.4Numerical and modelling errors
1.5Aleatory and epistemic uncertainty
1.6Context of use
1.7Risk associated with decisions
1.8Credibility maturity scale
1.9Cases of model-related failures
1.10Case study: credibility analysis of a structural simulation

2.1Testing of scientific software
2.2Method of manufactured solutions
2.3Observed order of convergence
2.4Regression testing
2.5Conservation tests
2.6Code benchmarks
2.7Version control and traceability
2.8Continuous integration for simulation
2.9Common implementation errors
2.10Lab: manufactured solutions in a heat solver

3.1Discretisation error
3.2Mesh independence studies
3.3Richardson extrapolation
3.4Grid convergence index (GCI)
3.5Iteration error
3.6Round-off error
3.7Time step and stability
3.8A posteriori error estimators
3.9Verification documentation
3.10Practical: GCI of a CFD simulation

4.1Design of validation experiments
4.2Validation hierarchy
4.3Experimental uncertainty
4.4Validation metrics
4.5Area metric
4.6Validation with sparse data
4.7Uncertain boundary conditions
4.8Comparison across multiple variables
4.9Validation report
4.10Lab: validation of a beam model with tests

5.1Probabilistic representation
5.2Monte Carlo propagation
5.3Latin hypercube and quasi-Monte Carlo
5.4Polynomial chaos
5.5Stochastic collocation
5.6Epistemic uncertainty and p-boxes
5.7Evidence theory
5.8Calculation of probability of failure
5.9Uncertainty in multiscale models
5.10Practical: propagation with polynomial chaos

6.1Local sensitivity and derivatives
6.2The Morris method
6.3Sobol indices
6.4Variance-based sensitivity
6.5FAST methods
6.6Sensitivity with surrogates
6.7Selection of influential parameters
6.8Visualisation of sensitivities
6.9Use in model reduction
6.10Lab: Sobol indices with SALib

7.1Bayesian inference
7.2Likelihood and priors
7.3Markov chain Monte Carlo
7.4The Kennedy–O'Hagan framework
7.5Model discrepancy
7.6Parameter identifiability
7.7Calibration with surrogates
7.8Sequential calibration
7.9Convergence diagnostics
7.10Practical: Bayesian calibration with PyMC

8.1Gaussian processes
8.2Polynomial regression
8.3Neural networks as surrogates
8.4Reduced-order models
8.5Design of computer experiments
8.6Adaptive sampling
8.7Surrogate validation
8.8Multi-fidelity
8.9Surrogate uncertainty
8.10Lab: surrogate of a thermal model

9.1ASME V&V 10 for solid mechanics
9.2ASME V&V 20 for CFD and heat transfer
9.3ASME V&V 40 for medical devices
9.4NASA-STD-7009
9.5Sandia's PCMM framework
9.6FDA guidance on modelling
9.7Standards for virtual homologation
9.8Documentation of evidence
9.9Peer review of models
9.10Case study: credibility plan to ASME V&V 40

10.1Virtual testing in the automotive industry
10.2Aeronautical certification by analysis
10.3In silico medical devices
10.4Nuclear safety and models
10.5Structural engineering and the Eurocodes
10.6Climate and energy models
10.7Digital twins
10.8AI models and their validation
10.9Lessons from projects
10.10Workshop: V&V strategy for a digital twin

11.1Margins and safety factors
11.2Reliability-based design
11.3Quantification of margins and uncertainties (QMU)
11.4Communicating uncertainty
11.5Value of information
11.6Test planning
11.7Risk and decision management
11.8Model auditing
11.9Model governance in the organisation
11.10Practical: margin analysis for a design decision

12.1Model and context of use
12.2Credibility plan
12.3Code verification
12.4Solution verification
12.5Validation with data
12.6Uncertainty quantification
12.7Sensitivity and calibration
12.8Credibility assessment
12.9Master's Thesis report
12.10Master's Thesis: defence before a panel

Methodology, software & labs

How you'll learn

Training is 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 the programme fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Dakota, UQLab, SALib, PyMC and OpenTURNS, in free, academic or evaluation versions. Testing is studied through real procedures: mesh convergence studies, experimental validation campaigns and Monte Carlo uncertainty propagation. Assessment is continuous for each module, plus the final Master's Thesis.

Standards covered: ASME V&V 10, ASME V&V 20, ASME V&V 40, NASA-STD-7009, ISO/IEC Guide 98-3 (GUM), JCGM 101, AIAA G-077.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.

Standards-based learning:guided reading of ASME V&V 10, ASME V&V 20 and ASME V&V 40 applied to real cases, not as a theoretical annex.

Industry software:Dakota, UQLab, SALib, PyMC and OpenTURNS, in free, academic or evaluation versions.

Master's Thesis with defence:A full credibility assessment of a computational model, from context of use and verification to validation, uncertainty quantification and calibration, 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.

Model and credibility plan

Define the model, its context of use and the credibility plan.

Code and solution verification

Verify the code and the numerical solution.

Validation with data

Validate the model against experimental data.

UQ, sensitivity and calibration

Quantify uncertainty, analyse sensitivity and calibrate the model.

Master's Thesis

Complete credibility assessment of a computational model, defended before a panel.

Career opportunities

Where this takes you

Verification and validation engineerUncertainty quantification specialistSimulation engineerModel credibility analystComputational reliability engineerComputational science researcher

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 Dakota, UQLab, SALib, PyMC and OpenTURNS, 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 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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