Master’s in Digital Twins for Devices
Online master's programme of 1,900 hours in digital twins for devices (edge-cloud sync, simulation and continuous testing). 12 modules covering digital twin concepts, physical and data-driven modelling, co-simulation with FMI, data acquisition and synchronisation, state estimation, diagnostics and prognostics, platforms, visualisation, optimisation and control, and verification, validation and governance, with an assigned technical tutor.
Master’s in Digital Twins for Devices — studying from Singapore
Singapore is one of Asia's leading electronics manufacturing centres, with semiconductor fabrication, back-end assembly and test, and precision electronics concentrated in areas such as Tampines and Jurong. The sector also includes embedded systems for industrial and automotive use and a strong telecommunications base led by the Infocomm Media Development Authority. Major employers include global semiconductor firms and electronics manufacturers. Engineers work on hardware design, firmware, communications systems and test engineering.
Fees for Singapore: S$9,880 (Singapore dollar), with instalments and employer sponsorship available.
Relevant bodies in Singapore
Typical roles
Questions from Singapore
How are electronics and telecom engineers recognised in Singapore, and does SEIUM count?
Telecom and electronics engineering is regulated by the Infocomm Media Development Authority (IMDA), and engineers may register with the Professional Engineers Board (PEB). SEIUM's qualifications are private, not state degrees, and not accredited by any national body. Whether they are recognised is decided by employers or the relevant body, so confirm this before enrolling.
Which Singapore employers hire embedded systems and telecom engineers?
Singapore has demand from telecom operators, semiconductor and electronics manufacturers, defence and aerospace firms, and industrial automation companies. Employers generally assess hands-on skills in firmware, hardware design and testing. Your final project can evidence embedded or telecom competence, but its value is for each employer to judge.
Can I follow this from Singapore while working, and does it need a visa?
Yes, the programmes are 100% online and taught in English, so no student visa is needed to study from home in Singapore. Expect a heavy workload: 1,900 hours for a master's, 800 for a postgraduate diploma and 300 for a specialist course. Fees can be paid in instalments, and employer sponsorship is possible.
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
Define the architecture of a digital twin according to ISO 23247.
Develop physical, data-driven and hybrid models of devices.
Integrate heterogeneous models using the FMI standard.
Synchronise the twin with real data through state estimation.
Implement diagnostics and remaining useful life prognostics.
Deploy twins on cloud and edge platforms.
Validate the credibility of the twin with V&V methodologies.
Who it's for
- Digital twin engineers.
- Modelling and simulation engineers moving from offline models to live twins.
- Industrial data scientists working with device data.
- IoT platform architects.
- Reliability engineers interested in model-based prognostics.
- Predictive maintenance engineers.
- Engineering or science graduates aiming to enter power electronics.
What you'll work with
- ISO 23247 (digital twin framework for manufacturing)
- IEC 63278-1 (Asset Administration Shell) (structure of the asset administration shell)
- Modelica Association FMI 3.0 (Functional Mock-up Interface for model exchange and co-simulation)
- ASME V&V 40 (credibility assessment of computational models)
- IEC 62541 (OPC UA) (unified architecture for industrial communication)
- ISO/IEC 30173 (digital twin concepts and terminology)
- ISO 13374 (condition monitoring and diagnostics of machines: data processing)
- MATLAB/Simulink
- OpenModelica
- Azure Digital Twins
- Eclipse Ditto
- Grafana
12 modules
1.1From simulation model to digital twin1.2Digital twin maturity levels1.3Digital shadow, model and twin1.4Product and device lifecycle1.5ISO 23247 reference architecture1.6Use cases in electronics and devices1.7Business value and return on investment1.8Fidelity and latency requirements1.9Interoperability challenges1.10Case study: scoping the digital twin of an inverter
2.1Multi-domain modelling with Modelica2.2Circuit models with SPICE2.3Lumped-parameter thermal models2.4Electromechanical modelling of actuators2.5Battery and cell models2.6Model order reduction2.7Ageing and degradation models2.8Model validation against tests2.9Managing model uncertainty2.10Practical exercise: thermal model of a power module
3.1Regression and black-box models3.2Neural networks for time series3.3Hybrid physics and data models3.4Physics-informed neural networks3.5System identification3.6Gaussian processes for surrogate models3.7Data drift detection3.8Model interpretability3.9Retraining and versioning3.10Lab: surrogate model of a sensor
4.1The FMI standard and FMUs4.2Co-simulation versus model exchange4.3The SSP standard for systems4.4Co-simulation masters4.5Integrating Simulink and Modelica4.6Time-step synchronisation4.7Real-time models for HIL4.8Managing dependencies between FMUs4.9Validation of the coupled system4.10Practical exercise: co-simulation of two FMUs with FMPy
5.1Instrumenting the physical device5.2OPC UA and MQTT protocols5.3Edge computing for pre-processing5.4Time synchronisation and timestamps5.5Data quality and cleansing5.6Time-series databases5.7Asset Administration Shell5.8Semantic modelling of assets5.9Security of the data chain5.10Lab: data flow from sensor to InfluxDB
6.1State observers6.2Linear and extended Kalman filters6.3Unscented Kalman filter6.4Particle filters6.5Data assimilation in the twin6.6Online parameter estimation6.7Multi-sensor fusion6.8Sensor fault detection6.9Continuous tuning of the twin6.10Practical exercise: estimating the state of charge of a battery
7.1Condition monitoring7.2Anomaly detection7.3Model-based fault diagnosis7.4Device health indicators7.5Remaining useful life estimation7.6Reliability models and Weibull analysis7.7Prognostics with machine learning7.8Condition-based maintenance7.9Prognostic evaluation metrics7.10Case study: remaining useful life of electrolytic capacitors
8.1Azure Digital Twins and DTDL8.2AWS IoT TwinMaker8.3Eclipse Ditto and open-source solutions8.4Microservices and containers8.5Orchestration with Kubernetes8.6APIs and event models8.7Scalability across device fleets8.8Platform cybersecurity8.9Cloud operating costs8.10Lab: device twin in Eclipse Ditto
9.1Dashboards with Grafana9.23D models and interactive scenes9.3Augmented reality for maintenance9.4Unity and Omniverse graphics engines9.5Alerts and event management9.6Operator-centred design9.7What-if scenario simulation9.8Bidirectional interaction with the asset9.9Accessibility and usability9.10Practical exercise: monitoring dashboard for a twin
10.1Optimisation of operating parameters10.2Model predictive control10.3Virtual firmware testing10.4Validating updates before deployment10.5Design of virtual experiments10.6Automatic parameter calibration10.7Multi-objective optimisation of operation10.8Reinforcement learning on the twin10.9Assessment of operational benefit10.10Workshop: control tuning using the twin
11.1Model credibility according to ASME V&V 4011.2Code and solution verification11.3Experimental validation and metrics11.4Configuration management of the twin11.5Traceability between requirements and model11.6Data governance and ownership11.7Legal and liability aspects11.8Documentation for audit11.9Long-term maintenance of the twin11.10Case study: V&V plan for a medical device
12.1Selection of the device and its context12.2Definition of the twin use cases12.3Data and model architecture12.4Development of the physical or hybrid model12.5Integration with real data12.6Implementation of analytics services12.7Validation of the twin12.8Assessment of the value delivered12.9Master's Thesis 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 fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB/Simulink, OpenModelica, Azure Digital Twins, Eclipse Ditto and Grafana, in free, academic or evaluation versions. Real procedures are studied: model validation against test data, co-simulation of FMU components and remaining useful life estimation. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ISO 23247, IEC 63278-1 (Asset Administration Shell), Modelica Association FMI 3.0, ASME V&V 40, IEC 62541 (OPC UA), ISO/IEC 30173, ISO 13374.
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
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- Internet connection and a computer able to run 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 MATLAB/Simulink, OpenModelica, Azure Digital Twins, Eclipse Ditto 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 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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