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Master'sNuclear Engineering & Nuclear Fuel Cycle Technologies Applications open

Digital Twins and Analytics for Nuclear Power Plants Engineering

Online master's programme of 1,900 hours in digital twins and analytics engineering for nuclear power plants, structured in 12 modules covering plant digitalisation, plant data, physical modelling, digital twin architecture, advanced analytics, condition monitoring, asset and ageing management, human factors, cybersecurity and predictive maintenance, 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 digitalisation strategies for nuclear power plants.

02

Manage and validate plant data.

03

Develop physical and data-driven system models.

04

Build digital twin architectures.

05

Apply advanced analytics to equipment monitoring.

06

Integrate ageing and asset management.

07

Meet regulatory and cybersecurity requirements.

Profile

Who it's for

  • Digital twin engineers.
  • Plant data scientists.
  • Condition monitoring engineers.
  • Asset management engineers.
  • OT cybersecurity engineers.
  • Digital transformation managers in the energy sector.
  • Engineering or science graduates seeking to enter the nuclear energy sector.
Standards & tools

What you'll work with

  • IEC 62645 (cybersecurity requirements for nuclear I&C systems)
  • IAEA Nuclear Security Series No. 17 (computer security at nuclear facilities)
  • NUREG-0711 (human factors engineering programme review model)
  • ISO 55000 (asset management)
  • IAEA SSG-48 (ageing management and long-term operation)
  • ISO 15926 (integration of life-cycle data for process plants)
  • IEC 60880 (software for safety-critical nuclear I&C systems)
  • AVEVA PI System
  • Modelica/Dymola
  • Python
  • scikit-learn
  • Grafana
Syllabus

12 modules · 1900 h

  1. 1.1Digital transformation in the nuclear sector
  2. 1.2The digital twin concept and maturity levels
  3. 1.3Use cases: operation, maintenance and training
  4. 1.4Plant systems architecture
  5. 1.5Digital instrumentation and control
  6. 1.6Regulatory challenges of digitalisation
  7. 1.7The LWRS sustainability programme
  8. 1.8Benefits and economic return
  9. 1.9Change management
  10. 1.10Case study: digital roadmap for a power plant
  1. 2.1Acquisition systems and PI System
  2. 2.2Process historians
  3. 2.3Data quality and validation
  4. 2.4Process and vibration signals
  5. 2.5Maintenance data and CMMS
  6. 2.6Inspection data
  7. 2.7Integration of heterogeneous sources
  8. 2.8ISO 15926 data models
  9. 2.9Data governance
  10. 2.10Lab: time-series analysis with pandas
  1. 3.1Thermal-hydraulic system models
  2. 3.2Full-scope simulators
  3. 3.3Reduced-order models
  4. 3.4Component models: pumps, heat exchangers and turbines
  5. 3.5Modelica for energy systems
  6. 3.6First-principles versus data-driven models
  7. 3.7Validation with plant data
  8. 3.8Model updating
  9. 3.9Simulation tools
  10. 3.10Practical exercise: secondary cycle model in Modelica
  1. 4.1Twin layers: physical, data, model and service
  2. 4.2Synchronisation with the plant
  3. 4.3Data assimilation
  4. 4.4Commercial and open platforms
  5. 4.5Integration with 3D models and BIM
  6. 4.6Interoperability and standards
  7. 4.7Cloud and on-premises architectures
  8. 4.8Scalability
  9. 4.9Maintaining the twin
  10. 4.10Workshop: architecture of a twin for an auxiliary system
  1. 5.1Statistics applied to processes
  2. 5.2Anomaly detection
  3. 5.3Principal component analysis
  4. 5.4Supervised learning for prediction
  5. 5.5Time series and forecasting
  6. 5.6Online sensor validation
  7. 5.7Equipment diagnostics
  8. 5.8Model explainability
  9. 5.9Model deployment
  10. 5.10Lab: anomaly detection with scikit-learn
  1. 6.1Monitoring of rotating equipment
  2. 6.2Vibration analysis
  3. 6.3Thermography
  4. 6.4Oil analysis
  5. 6.5Acoustic monitoring
  6. 6.6Valve monitoring
  7. 6.7Online calibration of instrumentation
  8. 6.8Equipment reliability programmes
  9. 6.9Integration into the twin
  10. 6.10Practical exercise: diagnosis of a pump using vibration data
  1. 7.1Ageing management
  2. 7.2Long-term operation
  3. 7.3IGALL ageing management programmes
  4. 7.4Fatigue monitoring
  5. 7.5Obsolescence management
  6. 7.6Investment planning
  7. 7.7Asset management to ISO 55000
  8. 7.8Risk and criticality
  9. 7.9Performance indicators
  10. 7.10Case study: ageing management plan for a component
  1. 8.1Digital control rooms
  2. 8.2Operator support
  3. 8.3Alarm management
  4. 8.4Computer-based procedures
  5. 8.5Human factors to NUREG-0711
  6. 8.6Simulator-based training
  7. 8.7Virtual and augmented reality
  8. 8.8Situational awareness
  9. 8.9Interface evaluation
  10. 8.10Workshop: analysis of an alarm system
  1. 9.1Cybersecurity of nuclear digital systems
  2. 9.2IAEA guide NSS 17
  3. 9.3IEC 62645 standards
  4. 9.4Security zone architecture
  5. 9.5Data diodes and cloud connections
  6. 9.6Software qualification
  7. 9.7Regulatory guidance on AI
  8. 9.8Configuration management
  9. 9.9Audits
  10. 9.10Practical exercise: cybersecurity zone analysis
  1. 10.1Maintenance strategies
  2. 10.2Remaining useful life prognosis
  3. 10.3Optimisation of refuelling outages
  4. 10.4Risk-informed planning
  5. 10.5Spare parts management
  6. 10.6Thermal efficiency of the cycle
  7. 10.7Production optimisation
  8. 10.8Economic value
  9. 10.9International case studies
  10. 10.10Lab: remaining useful life model
  1. 11.1Implementation strategy
  2. 11.2Pilots and scaling up
  3. 11.3Suppliers and contracts
  4. 11.4Digital competencies
  5. 11.5Data culture
  6. 11.6Project management
  7. 11.7Value indicators
  8. 11.8Lessons learned
  9. 11.9Future trends
  10. 11.10Case study: business case for a digital twin
  1. 12.1Definition of the use case
  2. 12.2Analysis of available data
  3. 12.3Physical or data-driven modelling
  4. 12.4Twin architecture
  5. 12.5Analytics development
  6. 12.6Value assessment
  7. 12.7Regulatory and cybersecurity considerations
  8. 12.8Conclusions
  9. 12.9Master's Thesis: written 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 each 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 AVEVA PI System, Modelica/Dymola, Python, scikit-learn and Grafana, in free, academic or evaluation versions. Practical work is based on real procedures for analysing historical plant data, detecting equipment anomalies and modelling plant systems. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: IEC 62645, IAEA Nuclear Security Series No. 17, NUREG-0711, ISO 55000, IAEA SSG-48 (ageing management), ISO 15926, IEC 60880.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of IEC 62645, IAEA Nuclear Security Series No. 17 and NUREG-0711 applied to real cases, not as a theoretical annex.
Industry software:AVEVA PI System, Modelica/Dymola, Python, scikit-learn and Grafana, in free, academic or evaluation versions.
Master's Thesis with defence:A complete digital twin use case, from data analysis and modelling to analytics, value assessment and regulatory and cybersecurity considerations, 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.

Define the use case
Select a plant system and the operational or maintenance problem the digital twin will address.
Analyse the plant data
Collect, clean and validate historical data from PI System and maintenance records.
Build the model
Develop a physical model in Modelica or a data-driven model in Python to represent the system.
Develop the analytics
Implement anomaly detection or remaining useful life models and integrate them into the twin architecture.
Master's Thesis
Assess the value, address regulatory and cybersecurity requirements and present the complete twin, defended before a panel.
Career opportunities

Where this takes you

Digital twin engineerPlant data scientistCondition monitoring engineerAsset management engineerOT cybersecurity engineerDigital transformation manager
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • 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 AVEVA PI System, Modelica/Dymola, Python, scikit-learn 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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