Master’s in Device Digital Twins Engineering
Online master's programme of 1,900 hours in device digital twin engineering, structured in 12 modules covering digital twin concepts, physical and data-driven modelling, FMI co-simulation, the physical-digital link, state estimation, diagnostics and prognostics, twins in the design cycle, twin platforms, fleet twins and model validation, with an assigned technical tutor.
Master’s in Device Digital Twins Engineering — studying from India
India's electronics manufacturing and design sector is expanding rapidly, with strong clusters in Bengaluru, Hyderabad, Chennai, Pune and the Noida-Gurugram region. Semiconductor and electronics initiatives under the Ministry of Electronics and Information Technology are encouraging chip design and fabrication projects. Telecom equipment, consumer electronics and automotive electronics all create demand for embedded systems engineers. The Telecommunication Engineering Centre sets telecom standards, while the Bureau of Indian Standards covers electronics product norms. Engineers here design firmware, hardware platforms, communication systems and testing solutions for a wide range of products.
Fees for India: ₹1,050,000 (Indian Rupee), with instalments and employer sponsorship available.
Relevant bodies in India
Typical roles
Questions from India
Which bodies in India recognise electronics and telecom engineers?
Electronics and telecom engineers in India are recognised through the Institution of Electronics and Telecommunication Engineers (IETE), the Institution of Engineers (India), and IEEE India sections. Telecom regulation sits with the Department of Telecommunications, and product certification follows standards from the Bureau of Indian Standards. SEIUM's private qualifications are not accredited by a national body, so their value is for employers to judge.
Who employs embedded systems and telecom engineers in India?
Employers include electronics manufacturers, semiconductor and design firms in Bengaluru and Hyderabad, telecom operators like Airtel and Jio, defence electronics firms, and automotive electronics suppliers. A private online programme can add specialist depth, but hiring managers usually look at practical projects, firmware experience and relevant certifications alongside the qualification.
How can I balance this programme with a job in India, and what is the time commitment?
The programme is fully online and taught in English, so you can study from India without a student visa. Specialist courses take about 300 hours, postgraduate diplomas about 800 and master's about 1,900. Schedules are based on IST, you have an assigned technical tutor, and fees can be paid in instalments. Ask your employer whether they can sponsor the course.
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 scope and architecture of device digital twins.
Build multi-domain physical models of electronic devices.
Develop data-driven models and surrogate models.
Integrate simulations through co-simulation and FMI standards.
Synchronise the twin with the real device through state estimation.
Apply digital twins to diagnostics, prognostics and design.
Develop and validate a complete digital twin of a device.
Who it's for
- Digital twin engineers
- Simulation engineers
- Model-based systems engineers
- Industrial data scientists
- IoT platform architects
- Diagnostics and prognostics engineers
- Engineering or science graduates looking to move into manufacturing
What you'll work with
- ISO 23247-1 (digital twin framework for manufacturing)
- IEC 62832-1 (digital factory framework)
- Functional Mock-up Interface 3.0 (model exchange and co-simulation)
- IEC 63278-1 (Asset Administration Shell)
- ISO/IEC 30173 (digital twin concepts and terminology)
- ASME V&V 40 (credibility of computational models)
- OMG SysML v2 (systems modelling language)
- IEC 62541 (OPC UA)
- MATLAB/Simulink
- OpenModelica
- Azure Digital Twins
- Ansys Twin Builder
- NVIDIA Omniverse
12 modules · 1900 h
1.1Definitions and maturity levels1.2Digital model, shadow and twin1.3Product, process and system twins1.4The twin life cycle1.5Reference architectures1.6Business value and use cases1.7Components of a device twin1.8Fidelity and cost challenges1.9Industrial state of the art1.10Case study: defining the scope of a twin
2.1Equation-based modelling2.2Multi-domain modelling with Modelica2.3Modelling electrical and electronic systems2.4Thermal modelling of devices2.5Mechanical modelling and vibrations2.6Electrochemical battery models2.7Model order reduction2.8Validation against measurements2.9Parameter calibration2.10Practical exercise: thermal model of a board in Modelica
3.1System identification3.2Grey-box models3.3Regression and supervised learning3.4Neural networks for dynamics3.5Physics-informed networks3.6Time-series models3.7Uncertainty quantification3.8Surrogate models of simulations3.9Model selection and validation3.10Lab: surrogate model with neural networks
4.1The FMI standard and FMUs4.2Co-simulation of heterogeneous models4.3Real-time simulation4.4Software-in-the-loop (SIL) testing4.5Hardware-in-the-loop (HIL) integration4.6Firmware simulation with Renode4.7Integration with system simulation environments4.8Scaling simulations in the cloud4.9Verification of results4.10Workshop: FMI co-simulation of a device and its controller
5.1Data acquisition from devices5.2State synchronisation5.3Telemetry protocols for twins5.4Information modelling with DTDL5.5Twins with the Asset Administration Shell5.6Ontologies and semantics5.7Latency and update frequency5.8Input data quality5.9Link security5.10Practical exercise: device twin with Azure Digital Twins
6.1State observers6.2The Kalman filter and its variants6.3Data assimilation6.4Online parameter estimation6.5Detecting model deviations6.6Sensor fusion in the twin6.7Virtual sensors derived from the model6.8State-of-health estimation6.9Robustness to missing data6.10Lab: state-of-charge estimation for a battery
7.1Model-based fault detection7.2Residual-based diagnostics7.3Remaining useful life prognosis7.4Predictive maintenance with twins7.5What-if scenario analysis7.6Operational optimisation7.7Twin-based predictive control7.8Validation of firmware updates7.9Decision support7.10Case study: residual-based diagnosis of a converter
8.1Model-based systems engineering (MBSE)8.2SysML v2 models8.3Design twin and virtual verification8.4Virtual compliance testing8.5Design optimisation8.6Traceability between model and requirements8.7The product digital thread8.8Product life cycle management (PLM)8.9Field feedback into design8.10Workshop: SysML architecture of a connected device
9.1Commercial twin platforms9.2Twins at the edge and in the cloud9.3Microservice architectures for twins9.4Graph databases9.53D visualisation and augmented reality9.6NVIDIA Omniverse and USD9.7Scalability for fleets9.8Interoperability between platforms9.9Operating costs9.10Practical exercise: real-time 3D state visualisation
10.1Individual and fleet twins10.2Aggregation and comparison10.3Detecting atypical behaviour10.4Analysis of hardware variants10.5Battery twins across fleets10.6Maintenance planning at scale10.7Deployment simulation10.8Communication network twins10.9Fleet value indicators10.10Case study: twin of an electric scooter fleet
11.1Model verification and validation11.2Model credibility according to ASME V&V11.3Model version management11.4Twin cybersecurity11.5Ownership of data and models11.6ISO 23247 standardisation11.7Regulatory use of simulated evidence11.8Ethics and transparency11.9Twin governance within the organisation11.10Workshop: validation plan for a twin
12.1Selection of the device and the twin's objectives12.2Twin architecture12.3Physical and data-driven modelling12.4Integration with real data12.5State estimation and diagnostics12.6Platform and visualisation12.7Validation and value analysis12.8Conclusions on the digital twin12.9Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. 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 MATLAB/Simulink, OpenModelica, Azure Digital Twins, Ansys Twin Builder and NVIDIA Omniverse, in free, academic or evaluation versions. Practical work is based on real procedures: multi-domain modelling and co-simulation of devices, state estimation with real sensor data and validation of models against laboratory measurements. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ISO 23247-1, IEC 62832-1, Functional Mock-up Interface 3.0, IEC 63278-1, ISO/IEC 30173, ASME V&V 40, OMG SysML v2, IEC 62541.
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 capable of running the programme's 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, Ansys Twin Builder 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 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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