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Master'sRobotics, Automation & Control Applications open

Master’s in Digital Twins, Simulation and MBSE Engineering

Online master's programme of 1,900 hours in digital twins, simulation and MBSE engineering, structured in 12 modules covering systems thinking and MBSE, SysML and Arcadia modelling, digital twin concepts, physical and discrete-event simulation, plant connectivity, predictive twins, virtual commissioning, 3D environments and model-based V&V, 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

Apply the principles of model-based systems engineering.

02

Model requirements and architectures with SysML.

03

Define digital twins in line with standards.

04

Develop physical and discrete-event simulation models.

05

Connect digital twins to plant data.

06

Carry out virtual commissioning of automated systems.

07

Plan the organisational implementation of MBSE.

Profile

Who it's for

  • MBSE systems engineers adopting SysML and Arcadia.
  • Digital twin specialists building connected plant and product models.
  • Simulation engineers working on physical or discrete-event models.
  • Automation systems architects designing complex installations.
  • Virtual commissioning engineers testing control logic before start-up.
  • Digital transformation consultants guiding model-based adoption.
  • Engineering or science graduates seeking to enter the automotive sector.
Standards & tools

What you'll work with

  • ISO/IEC/IEEE 15288:2023 (system life cycle processes)
  • ISO 23247-1:2021 (digital twin framework for manufacturing)
  • IEC 63278-1:2023 (Asset Administration Shell structure)
  • OMG SysML v1.7 (Systems Modeling Language)
  • FMI 3.0 (Functional Mock-up Interface for model exchange and co-simulation)
  • IEC 62541-1:2020 (OPC Unified Architecture — overview and concepts)
  • ISO/IEC/IEEE 42010:2022 (architecture description)
  • IEC 62264-1:2013 (enterprise-control system integration, ISA-95 models)
  • Cameo Systems Modeler
  • Capella
  • MATLAB/Simulink
  • Siemens Plant Simulation
  • NVIDIA Omniverse
Syllabus

12 modules · 1900 h

  1. 1.1Systems engineering according to ISO/IEC/IEEE 15288
  2. 1.2From document-based to model-based approaches
  3. 1.3V life cycle and agile approaches
  4. 1.4Requirements, architecture and verification
  5. 1.5Traceability between artefacts
  6. 1.6Benefits and barriers of MBSE
  7. 1.7INCOSE Vision 2035
  8. 1.8Organisational maturity in MBSE
  9. 1.9Success stories in automation
  10. 1.10Case study: analysis of a mechatronic system
  1. 2.1SysML v1 and its diagrams
  2. 2.2What is new in SysML v2
  3. 2.3Requirements and use case diagrams
  4. 2.4Blocks and internal block diagrams
  5. 2.5Activity and state machine diagrams
  6. 2.6Parametrics and constraints
  7. 2.7OOSEM and Arcadia methodologies
  8. 2.8Capella and Arcadia modelling
  9. 2.9Cameo and Enterprise Architect tools
  10. 2.10Lab: SysML model of a robotic cell
  1. 3.1Definitions of a digital twin
  2. 3.2Digital model, shadow and twin
  3. 3.3Digital twin maturity levels
  4. 3.4ISO 23247 for manufacturing
  5. 3.5Asset Administration Shell
  6. 3.6Product, process and plant twins
  7. 3.7Synchronisation with the physical asset
  8. 3.8Reference architectures
  9. 3.9Business value of the twin
  10. 3.10Practical exercise: defining a twin with AAS
  1. 4.1Multi-domain modelling with Modelica
  2. 4.2Simscape for mechatronic systems
  3. 4.3Multibody simulation of machines
  4. 4.4Finite element simulation
  5. 4.5Fluid and thermal simulation
  6. 4.6Reduced-order models
  7. 4.7The FMI standard and co-simulation
  8. 4.8Validating models against measurements
  9. 4.9Real-time performance
  10. 4.10Workshop: FMI co-simulation of a drive
  1. 5.1Modelling production lines
  2. 5.2Plant Simulation and FlexSim
  3. 5.3Statistical distributions of processes
  4. 5.4Capacity and bottleneck analysis
  5. 5.5Experiments and optimisation
  6. 5.6Intralogistics simulation
  7. 5.7Agent-based simulation
  8. 5.8Statistical validation of the model
  9. 5.9Integration with real data
  10. 5.10Practical exercise: line model in Plant Simulation
  1. 6.1Real-time data acquisition
  2. 6.2OPC UA as the backbone
  3. 6.3MQTT and the Unified Namespace
  4. 6.4Time-series databases
  5. 6.5Semantic data models
  6. 6.6State synchronisation
  7. 6.7Continuous model calibration
  8. 6.8Latencies and update rates
  9. 6.9IIoT platforms for twins
  10. 6.10Lab: twin synchronised via OPC UA
  1. 7.1Model-based state estimation
  2. 7.2Twin-based predictive maintenance
  3. 7.3Hybrid physics and data models
  4. 7.4Machine learning in twins
  5. 7.5Optimisation of process parameters
  6. 7.6What-if scenario analysis
  7. 7.7Anomaly detection
  8. 7.8Asset health indicators
  9. 7.9Explainability of predictions
  10. 7.10Case study: predictive twin of a bearing
  1. 8.1Concept and benefits
  2. 8.2Machine behaviour models
  3. 8.3Software-in-the-loop (SIL) testing
  4. 8.4Hardware-in-the-loop (HIL) testing
  5. 8.5NX MCD and Process Simulate tools
  6. 8.6PLC and robot emulation
  7. 8.7Sequence and fault testing
  8. 8.8Reducing commissioning time
  9. 8.9Limitations of virtual commissioning
  10. 8.10Practical exercise: virtual commissioning of a conveyor
  1. 9.1Unity and Unreal game engines
  2. 9.2NVIDIA Omniverse and OpenUSD
  3. 9.3Virtual reality for design reviews
  4. 9.4Augmented reality on the plant floor
  5. 9.53D data visualisation
  6. 9.6Performance in large scenes
  7. 9.7Remote collaboration in the twin
  8. 9.8Operator training with twins
  9. 9.9User experience
  10. 9.10Workshop: OpenUSD scene of a cell
  1. 10.1Requirements verification with simulation
  2. 10.2Model-based testing
  3. 10.3Requirements coverage
  4. 10.4Managing model credibility
  5. 10.5Simulation validation standards
  6. 10.6Uncertainty analysis
  7. 10.7Traceability of evidence
  8. 10.8Integration into quality processes
  9. 10.9Certification with models
  10. 10.10Case study: model-based V&V plan
  1. 11.1MBSE and digital twin roadmap
  2. 11.2PLM engineering data management
  3. 11.3Integration with ALM
  4. 11.4Model governance
  5. 11.5Skills and training
  6. 11.6Costs and return on investment
  7. 11.7Vendors and ecosystem
  8. 11.8Open standards and interoperability
  9. 11.9Lessons from real implementations
  10. 11.10Practical exercise: adoption roadmap
  1. 12.1Definition of the target system
  2. 12.2SysML model of requirements and architecture
  3. 12.3Physical simulation models
  4. 12.4Production simulation
  5. 12.5Connection to real data
  6. 12.6Twin analytics
  7. 12.7Verification and validation
  8. 12.8Conclusions and scaling
  9. 12.9Master's Thesis report
  10. 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 material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so it is compatible with full-time work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Cameo Systems Modeler, Capella, MATLAB/Simulink, Siemens Plant Simulation and NVIDIA Omniverse, in free, academic or evaluation versions. Exercises are based on real procedures for SysML modelling of automated systems, co-simulation of physical models and virtual commissioning of installations. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO/IEC/IEEE 15288:2023, ISO 23247-1:2021, IEC 63278-1:2023, OMG SysML v1.7, FMI 3.0, IEC 62541-1:2020, ISO/IEC/IEEE 42010:2022, IEC 62264-1:2013.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO/IEC/IEEE 15288, ISO 23247 and IEC 63278 applied to real cases, not as a theoretical annex.
Industry software:Cameo Systems Modeler, Capella, MATLAB/Simulink, Siemens Plant Simulation and NVIDIA Omniverse, in free, academic or evaluation versions.
Master's Thesis with defence:A connected digital twin of an automated system, built on an MBSE model, 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 the system in SysML
Define the target system and model its requirements and architecture.
Build the simulation models
Develop physical and production simulation models of the system.
Connect to real data
Synchronise the twin with plant data via OPC UA or MQTT.
Analyse and validate the twin
Apply twin analytics and a model-based verification and validation plan.
Master's Thesis
A connected digital twin of an automated system, built on an MBSE model, defended before a panel.
Career opportunities

Where this takes you

MBSE systems engineerDigital twin specialistSimulation engineerAutomation systems architectVirtual commissioning engineerDigital transformation consultant
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 Cameo Systems Modeler, Capella, MATLAB/Simulink, Siemens Plant Simulation and NVIDIA Omniverse, 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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