Master’s in Off-Road Telemetry and Data Engineering
Online master's programme of 1,900 hours in off-road telemetry and data engineering. 12 modules covering off-road telemetry, sensors and instrumentation, acquisition and logging, signal processing, data pipelines, performance analysis, data-driven reliability, machine learning, dashboards, industrial machinery data and data governance, with an assigned technical tutor.
Master’s in Off-Road Telemetry and Data Engineering — studying from the United States
Off-road and specialty vehicle engineering is a strong part of American manufacturing, with heavy equipment and agricultural machinery produced across Illinois, Wisconsin, Iowa, Texas and North Carolina. Companies such as Caterpillar, Deere & Company and Oshkosh Corporation design construction, agricultural, mining and specialty vehicles. Engineers focus on hydraulics, powertrains, operator ergonomics and durability under demanding conditions. Compliance with emissions and safety standards shapes much of the design process.
Fees for the United States: US$8,850 (US Dollars), with instalments and employer sponsorship available.
Relevant bodies in the United States
Typical roles
Questions from the United States
What US bodies are relevant to off-road and special vehicle engineering?
SAE International is central, publishing off-road and heavy machinery standards. Machinery safety is addressed by OSHA, and emissions by the EPA. Professional PE licensure is granted by state boards. SEIUM's qualifications are private and not accredited, so recognition for particular roles is decided by employers or the relevant body.
Where do off-road and special vehicle engineers work in the United States?
Employers include agricultural, construction, and mining equipment manufacturers, defense and specialty vehicle builders, and off-road accessory companies. Roles include design, testing, and drivetrain engineering. Employers evaluate applied skill and experience, so use your final project to show practical competence to hiring managers.
How flexible is the programme for working professionals in the US?
The programmes are 100% online and taught in English, so you can study from home without a student visa. Workloads are 300, 800, or 1,900 hours depending on the programme type, so plan your time accordingly. Fees are payable in instalments, and employer sponsorship may be possible, so ask your employer early.
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
Design instrumentation plans for off-road vehicles.
Configure acquisition and logging systems.
Process signals and create math channels.
Build scalable data pipelines.
Analyse performance and reliability from data.
Apply machine learning to telemetry.
Govern data according to quality and security criteria.
Who it's for
- Motorsport data engineers
- Telemetry engineers
- Industrial data engineers
- Performance analysts
- Instrumentation engineers
- Data scientists working on vehicles
- Engineering or science graduates looking to enter off-road motorsport
What you'll work with
- SAE J1939 (heavy-vehicle communication network)
- ISO 11898 (CAN bus)
- ASAM MDF (measurement data format)
- ISO 15143-3 (telematics data exchange for machinery)
- Regulation (EU) 2016/679 (General Data Protection Regulation)
- Regulation (EU) 2023/2854 (Data Act)
- MoTeC i2
- Python with pandas
- Apache Parquet
- Grafana
- MATLAB
12 modules · 1900 h
1.1Motorsport versus industrial telemetry1.2Objectives of data acquisition1.3Typical variables of an off-road vehicle1.4Sampling rates by signal1.5On-board data loggers1.6Real-time telemetry1.7Bandwidth constraints1.8Use cases in development and operation1.9Organising a data team1.10Case study: acquisition plan for a rally-raid vehicle
2.1Suspension travel potentiometers2.2Accelerometers and inertial units2.3Strain gauges on structures2.4Pressure and temperature sensors2.5Wheel speed sensors2.6High-frequency GNSS2.7Thermocouples on brakes and exhaust2.8Sensor calibration2.9Wiring and protection in harsh environments2.10Lab: calibrating a suspension potentiometer
3.1Architecture of the acquisition system3.2CAN buses as a data source3.3Time synchronisation of signals3.4Anti-aliasing pre-filtering3.5Log file formats3.6Robust on-board storage3.7Data download in the field3.8Managing logging configurations3.9Validating signal quality3.10Practical exercise: configuring a data logger
4.1Digital filter design4.2Frequency analysis4.3Integration and differentiation of signals4.4Event detection4.5Creating math channels4.6Resampling and alignment4.7Handling outliers4.8Data compression4.9Processing tools4.10Lab: math channels in Python
5.1Automatic log ingestion5.2Cloud storage5.3Time-series databases5.4Columnar formats such as Parquet5.5Distributed processing of logs5.6Task orchestration5.7Data versioning5.8Metadata and catalogues5.9Infrastructure costs5.10Practical exercise: ingestion pipeline in Python
6.1Lap and sector analysis6.2Driver comparison6.3Suspension analysis with histograms6.4Consumption analysis6.5Thermal analysis of components6.6Correlation with simulation6.7Reports for engineers and drivers6.8Key performance indicators6.9Report automation6.10Case study: report for a rally-raid stage
7.1Overload detection7.2Cycle counting and cumulative damage7.3Predicting failures during a race7.4Root cause analysis of failures7.5Monitoring critical components7.6Alarm limits7.7Integration with maintenance7.8Failure databases7.9Improving design with data7.10Practical exercise: cumulative damage in a suspension arm
8.1Terrain type classification8.2Sensor anomaly detection8.3Energy consumption models8.4Estimating unmeasured states8.5Degradation models8.6Cross-validation of models8.7Deploying models at the edge8.8Interpretability of results8.9Limitations with small datasets8.10Lab: terrain classifier using accelerations
9.1Principles of data visualisation9.2Telemetry analysis tools9.3Real-time dashboards9.4Maps and georeferenced data9.5Visualisation for drivers9.6Visualisation for technical management9.7Designing visual alerts9.8Report design9.9Visualisation performance9.10Practical exercise: dashboard in Grafana
10.1Telemetry in construction machinery10.2Duty cycle data10.3Consumption and emissions data10.4Operator and productivity data10.5Integration with manufacturer telematics10.6Machinery data standards10.7Privacy of operator data10.8Industrial use cases10.9Transfer from motorsport10.10Case study: cycle analysis of a loader
11.1Data ownership11.2Data quality11.3Data security11.4Access control11.5Personal data protection11.6Retention and deletion11.7The European Data Act11.8Documentation of datasets11.9Data audits11.10Practical exercise: data governance policy for a team
12.1Definition of the vehicle and objectives12.2Instrumentation plan12.3Acquisition system12.4Data pipeline12.5Performance analysis12.6Reliability or machine learning model12.7Dashboard12.8Technical conclusions of the study12.9Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
Training is 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 the intake, so the programme can be combined with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MoTeC i2, Python with pandas, Apache Parquet, Grafana and MATLAB, in free, academic or evaluation versions. Testing is studied through real procedures for off-road vehicle instrumentation, stage data analysis and telemetry data pipelines. Assessment is continuous for each module, plus the final Master's Thesis.
Standards covered: SAE J1939, ISO 11898, ASAM MDF, ISO 15143-3, Regulation (EU) 2016/679, Regulation (EU) 2023/2854.
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 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 MoTeC i2, Python with pandas, Apache Parquet, Grafana and MATLAB, 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 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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