Postgraduate Diploma in Test Design and Multi-Source DoE
Online 800-hour Postgraduate Diploma in Test Design and Multi-Source DoE. 8 modules covering ISO 3534-3 design of experiments vocabulary, ISO 5725 accuracy of measurement methods and Minitab. Assigned tutor and open enrolment.
Postgraduate Diploma in Test Design and Multi-Source DoE — studying from New Zealand
New Zealand has a modest automotive industry, with most vehicle engineering centred on aftermarket parts, commercial vehicle body building, and the import and distribution of vehicles. Motorsport is a significant sector, with Hamilton and the Waikato region hosting a dense cluster of racing teams and engineering firms, and the Motorsport New Zealand body governing the national championships. Automotive engineers commonly work for importers, dealer networks, fleet operators and specialist fabrication businesses, and the sector also serves the country's large agricultural vehicle market.
Fees for New Zealand: NZ$2,550 (New Zealand dollar), with instalments and employer sponsorship available.
Relevant bodies in New Zealand
Typical roles
Questions from New Zealand
Is automotive engineering a regulated title in New Zealand, and which bodies matter here?
The title Chartered Professional Engineer is regulated and administered by Engineering New Zealand, which sets competence standards for registration. Vehicle standards and certification in New Zealand are overseen by the NZ Transport Agency Waka Kotahi. SEIUM's qualifications are private and not accredited by a national body, so recognition by employers or Engineering New Zealand is not something SEIUM can promise.
What kinds of automotive and motorsport employers hire engineers in New Zealand?
New Zealand's automotive sector includes vehicle importers, workshops and fleet operators, motorsport teams and specialist fabrication and engineering firms, along with companies supplying parts and servicing. Roles often reward hands-on experience and clear technical reporting. A private qualification can support an application, but whether an employer values it is their decision and cannot be assumed.
How much time does a programme need, and can an employer help fund it?
Specialist courses take about 300 hours, postgraduate diplomas 800 hours and master's programmes 1,900 hours, all online in English with an assigned technical tutor and a final project. Fees are payable in instalments, and employer sponsorship is possible. Choose a pace you can sustain alongside work, and check with your employer early about any funding or study leave arrangements.
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
Apply statistical fundamentals to test analysis.
Design factorial and fractional factorial experiments.
Fit response surfaces and optimise multiple responses.
Combine data from different sources using multifidelity models.
Carry out global sensitivity analysis using computational methods.
Plan track tests with controlled noise variables.
Manage a decision-oriented experimental programme.
Who it's for
- Test engineer who wants to specialise in Test Design and Multi-Source DoE.
- Performance engineer who wants to specialise in Test Design and Multi-Source DoE.
- Data analyst who wants to specialise in Test Design and Multi-Source DoE.
- Aerodynamics engineer who wants to specialise in Test Design and Multi-Source DoE.
- Simulation engineer who wants to specialise in Test Design and Multi-Source DoE.
- Quality engineer who wants to specialise in Test Design and Multi-Source DoE.
- Graduates in engineering or sciences seeking to enter a competitive job market.
What you'll work with
- ISO 3534-3 design of experiments vocabulary
- ISO 5725 accuracy of measurement methods
- ISO 13053-1 Six Sigma DMAIC methodology
- ISO 16269 statistical interpretation of data
- JCGM 100 GUM Guide
- FIA aerodynamic testing restrictions
8 modules · 800 h
1.1Variability and experimental error1.2Distributions and estimation1.3Hypothesis testing1.4Confidence intervals1.5Statistical power and sample size1.6Multiple linear regression1.7Analysis of variance1.8Assumptions and residual diagnostics1.9Statistics applied to track data1.10Workshop: analysis of two wing configurations
2.1Factors, levels and responses2.2Full factorial designs 2^k2.3Main effects and interactions2.4Fractional designs and resolution2.5Confounding of effects2.6Blocking and randomisation2.7Centre points and curvature2.8Plackett-Burman screening designs2.9Interpreting effect plots2.10Practical: fractional factorial for set-up
3.1Central composite designs3.2Three-level Box-Behnken designs3.3Fitting quadratic models3.4Response optimisation3.5Desirability function for multiple responses3.6D- and I-optimal designs3.7Factor constraints3.8Model validation3.9Extrapolation errors3.10Laboratory: response surface with Design-Expert
4.1Sources: CFD, wind tunnel, rig, track and simulator4.2Fidelity and cost of each source4.3Low- and high-fidelity multifidelity models4.4Co-kriging and bias correction4.5Fusion of heterogeneous data4.6Cross-calibration between sources4.7Uncertainty by source4.8Test budget allocation4.9Regulatory testing constraints4.10Case study: combining wind tunnel and CFD data
5.1Latin Hypercube Sampling5.2Sobol sequences5.3Uncertainty-guided adaptive sampling5.4Metamodels with Gaussian processes5.5Global sensitivity analysis5.6Sobol indices5.7Metamodels for optimisation5.8Cross-validation of metamodels5.9Scaling to many factors5.10Practical: global sensitivity analysis in Python
6.1Constraints of a test day6.2Noise variables: wind, temperature, tyres6.3Repeated tests and replicates6.4Split-plot designs with hard-to-change factors6.5Test order and drift6.6Damper and engine rigs6.7Running-in data analysis6.8Decisions with small samples6.9Communicating results6.10Case study: one-day test with two drivers
7.1Annual programme planning7.2Hypotheses and success criteria7.3Experiment documentation7.4Results repository7.5Reproducibility of experiments7.6Internal peer review7.7Cost per information gained7.8Sequential Bayesian learning7.9Team experimental culture7.10Workshop: seasonal experimental plan
8.1Definition of the technical problem8.2Selection of factors and sources8.3Design of the multi-source experiment8.4Simulation or data collection8.5Fitting statistical models8.6Sensitivity analysis8.7Optimisation and validation8.8Recommendations to the team8.9Report and presentation8.10Final review with the tutor
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: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Minitab, JMP, Design-Expert, Python with SALib and scikit-learn, modeFRONTIER, in free, academic or evaluation versions. Tests are studied using real procedures from track test plans with factorial design, combining wind tunnel and CFD data, and global sensitivity analysis of models. Assessment is continuous, module by module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Apply statistical fundamentals to test analysis.
Design factorial and fractional factorial experiments.
Fit response surfaces and optimise multiple responses.
Combine data from different sources using multifidelity models.
Carry out global sensitivity analysis using computational methods.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical regulations.
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 comprises 800 hours in total, spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.
You will work with Minitab, JMP, Design-Expert, Python with SALib and scikit-learn, modeFRONTIER. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM diploma upon passing continuous assessment and the integrative project. This is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Check the recognition policy when you enrol.
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