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Master'sMotorcycles & 2-Wheelers Applications open

Master’s in Motorcycle Telemetry and Data Engineering

Online master's programme of 1,900 hours in motorcycle telemetry and data engineering, with 12 modules covering motorcycle telemetry, sensors and acquisition architecture, ASAM MDF ingestion and storage, signal processing, performance analysis, maths channels and models, machine learning, visualisation, live telemetry operations and data governance, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Design data acquisition systems for motorcycles.

02

Select and calibrate sensors for motorcycle channels.

03

Build pipelines for telemetry ingestion, storage and processing.

04

Develop maths channels and automated analyses.

05

Apply machine learning to racing data.

06

Visualise and communicate results to engineers and riders.

07

Develop a data engineering platform for a motorcycle racing team.

Profile

Who it's for

  • Racing data engineers in motorcycle teams.
  • Telemetry engineers working trackside or at the factory.
  • Motorcycle electronics engineers.
  • Motorsport data scientists.
  • Analysis software engineers.
  • Data acquisition systems engineers.
  • Engineering or science graduates aiming to join motorcycle racing teams.
Standards & tools

What you'll work with

  • ASAM MDF 4.x (measurement data format)
  • ASAM XCP (universal measurement and calibration protocol)
  • ISO 11898-1 (CAN data link layer)
  • IEEE 802.11 (wireless LAN)
  • ISO 8000-8 (data quality)
  • ISO/IEC 27001 (information security management)
  • FIM Grand Prix Regulations (MotoGP sporting and technical rules)
  • Regulation (EU) 2016/679 (GDPR, data protection)
  • MoTeC i2 Pro
  • 2D Data Recording
  • Python with pandas
  • Apache Kafka
  • Grafana
Syllabus

12 modules · 1900 h

  1. 1.1Evolution of data logging
  2. 1.2Logger versus live telemetry
  3. 1.3Championship regulatory restrictions
  4. 1.4Typical channels on a motorcycle
  5. 1.5Differences compared with cars
  6. 1.6Users of the data
  7. 1.7Data flow within a team
  8. 1.8Data volume per session
  9. 1.9Value of the data
  10. 1.10Case study: channel map of a superbike
  1. 2.1Wheel speed sensors
  2. 2.2Suspension potentiometers
  3. 2.3IMU and lean angle
  4. 2.4Brake pressure
  5. 2.5Tyre sensors
  6. 2.6High-frequency GPS
  7. 2.7Strain gauges on components
  8. 2.8Installation on the motorcycle
  9. 2.9Sensor calibration
  10. 2.10Lab: installing and calibrating a suspension sensor
  1. 3.1Loggers and ECUs
  2. 3.2CAN acquisition buses
  3. 3.3Sampling rates
  4. 3.4Time synchronisation of sources
  5. 3.5Wireless transmission in the garage
  6. 3.6File formats
  7. 3.7XCP calibration protocol
  8. 3.8System diagnostics
  9. 3.9Reliability of the data harness
  10. 3.10Practical: configuring a logger
  1. 4.1ASAM MDF format
  2. 4.2Format conversion
  3. 4.3Time-series databases
  4. 4.4Cloud storage
  5. 4.5Session metadata
  6. 4.6Data catalogue
  7. 4.7Data quality to ISO 8000
  8. 4.8Data versioning
  9. 4.9Retention policies
  10. 4.10Workshop: ingestion pipeline with Python
  1. 5.1Channel filtering
  2. 5.2Numerical derivatives and integrals
  3. 5.3Offset correction
  4. 5.4Sensor fusion
  5. 5.5Channel resampling
  6. 5.6Peak detection
  7. 5.7Segmentation by lap
  8. 5.8Alignment by distance
  9. 5.9Channel validation
  10. 5.10Practical: derived lean-angle channels
  1. 6.1Lap comparison
  2. 6.2Corner-by-corner analysis
  3. 6.3Braking analysis
  4. 6.4Traction analysis
  5. 6.5Time gained and lost
  6. 6.6Line analysis with GPS
  7. 6.7Rider-to-rider comparison
  8. 6.8Automated session reports
  9. 6.9Key performance indicators
  10. 6.10Case study: analysis of a practice session
  1. 7.1Maths channels in commercial tools
  2. 7.2Force estimation
  3. 7.3Slip estimation
  4. 7.4Fuel consumption models
  5. 7.5Simplified tyre models
  6. 7.6Lap models
  7. 7.7Correlation with simulation
  8. 7.8Reusable channel libraries
  9. 7.9Validation of results
  10. 7.10Lab: channel library in Python
  1. 8.1Corner classification
  2. 8.2Sensor anomaly detection
  3. 8.3Degradation prediction
  4. 8.4Clustering of riding styles
  5. 8.5Lap time regression models
  6. 8.6Feature engineering
  7. 8.7Model cross-validation
  8. 8.8Model interpretability
  9. 8.9Model deployment
  10. 8.10Practical: channel anomaly detector
  1. 9.1Dashboards for engineers
  2. 9.2Dashboards for riders
  3. 9.3Circuit map visualisation
  4. 9.4Lap charts
  5. 9.5Video synchronised with data
  6. 9.6Report design
  7. 9.7Real-time dashboards
  8. 9.8Communication in technical meetings
  9. 9.9Accessibility of information
  10. 9.10Workshop: session dashboard in Grafana
  1. 10.1Telemetry in the garage
  2. 10.2Wireless data links
  3. 10.3Latency and prioritisation
  4. 10.4Real-time alarms
  5. 10.5Remote support from the factory
  6. 10.6Server infrastructure
  7. 10.7Redundancy of servers and links
  8. 10.8Cybersecurity of the connected system
  9. 10.9Data protection
  10. 10.10Case study: data operations at a Grand Prix
  1. 11.1Data governance
  2. 11.2Data ownership with suppliers
  3. 11.3Rider data and privacy
  4. 11.4Managing data projects
  5. 11.5Roles in a data department
  6. 11.6Tools and licences
  7. 11.7User training
  8. 11.8Impact assessment
  9. 11.9Continuous improvement cycle
  10. 11.10Practical: data governance plan
  1. 12.1Team requirements
  2. 12.2Acquisition architecture
  3. 12.3Data pipeline
  4. 12.4Signal processing
  5. 12.5Performance analysis
  6. 12.6Machine learning model
  7. 12.7Visualisation of results
  8. 12.8System evaluation
  9. 12.9Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

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: you progress at your own pace within the intake, so you can combine it with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MoTeC i2 Pro, 2D Data Recording, Python with pandas, Apache Kafka and Grafana, in free, academic or evaluation versions. Tests are studied through real procedures: building a data platform for racing motorcycle telemetry, automated lap analysis with maths channels, and sensor anomaly detection with machine learning. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ASAM MDF 4.x, ASAM XCP, ISO 11898-1, IEEE 802.11, ISO 8000-8, ISO/IEC 27001, FIM Grand Prix Regulations, Regulation (EU) 2016/679.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ASAM MDF 4.x, ASAM XCP and ISO 11898-1 applied to real cases, not as a theoretical annex.
Industry software:MoTeC i2 Pro, 2D Data Recording, Python with pandas, Apache Kafka and Grafana, in free, academic or evaluation versions.
Master's Thesis with defence:A data engineering platform for a motorcycle racing team, from acquisition architecture and data pipeline to analysis, machine learning and visualisation, 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.

Requirements and architecture
Define team requirements and the acquisition architecture.
Data pipeline
Build the ingestion, storage and signal processing pipeline.
Performance analysis
Develop automated lap analysis with maths channels.
Machine learning and visualisation
Train an anomaly detection model and build dashboards.
Master's Thesis
A complete racing data platform, defended before a panel.
Career opportunities

Where this takes you

Racing data engineerTelemetry engineerMotorcycle electronics engineerMotorsport data scientistAnalysis software engineerData acquisition systems engineer
Admissions & scholarships

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.

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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 MoTeC i2 Pro, 2D Data Recording, Python with pandas, Apache Kafka and Grafana, 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 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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