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Master'sNuclear Engineering & Nuclear Fuel Cycle Technologies Applications open

Master’s in Data, Modelling & Simulation Engineering

Online master's programme of 1,900 hours in data, modelling and simulation engineering for the nuclear sector. 12 modules covering simulation in nuclear engineering, nuclear data, scientific programming, numerical methods, neutronic and thermal-hydraulic modelling, plant data analysis, machine learning, uncertainty quantification, verification and validation, and multiscale simulation and HPC, with ASME NQA-1, IAEA SSG-2 and the ENDF-6 Formats Manual, 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

Understand the calculation chain in nuclear engineering.

02

Process and use nuclear data libraries.

03

Apply numerical methods and high-performance programming.

04

Model neutronic and thermal-hydraulic systems.

05

Apply machine learning to nuclear data and models.

06

Quantify uncertainties with BEPU methodologies.

07

Verify and validate codes according to quality requirements.

Profile

Who it's for

  • Nuclear simulation engineers
  • Safety analysts
  • Scientific code developers
  • Plant data scientists
  • Uncertainty quantification engineers
  • Multiphysics researchers
  • Engineering or science graduates looking to enter the nuclear energy sector
Standards & tools

What you'll work with

  • ASME NQA-1 (quality assurance for nuclear facilities)
  • IAEA SSG-2 (deterministic safety analysis)
  • ENDF-6 Formats Manual (evaluated nuclear data formats)
  • ASME V&V 20 (verification and validation in computational fluid dynamics and heat transfer)
  • IAEA SSR-2/1 (safety of nuclear power plants: design)
  • 10 CFR 50 Appendix B (US quality assurance criteria for nuclear plants)
  • ISO/IEC 25010 (software product quality)
  • OpenMC
  • Serpent
  • RELAP5
  • DAKOTA
  • MOOSE
Syllabus

12 modules · 1900 h

  1. 1.1Role of simulation in licensing
  2. 1.2Calculation chain: nuclear data, lattice, core and system
  3. 1.3Deterministic and stochastic codes
  4. 1.4Thermal-hydraulic system codes
  5. 1.5Verification, validation and uncertainty quantification
  6. 1.6Nuclear software quality
  7. 1.7High-performance computing
  8. 1.8Open ecosystems: OpenMC and MOOSE
  9. 1.9OECD/NEA international benchmarks
  10. 1.10Case study: code map for a safety analysis
  1. 2.1Cross sections and resonances
  2. 2.2Evaluated libraries ENDF/B-VIII and JEFF-3.3
  3. 2.3Processing with NJOY
  4. 2.4ACE and multigroup formats
  5. 2.5Data covariances
  6. 2.6Decay data and fission yields
  7. 2.7Differential and integral experiments
  8. 2.8Sensitivity to nuclear data
  9. 2.9EXFOR database
  10. 2.10Lab: processing a cross section with NJOY
  1. 3.1Scientific Python: NumPy and SciPy
  2. 3.2Modern Fortran and C++ in nuclear codes
  3. 3.3Version control and good practice
  4. 3.4MPI and OpenMP parallelisation
  5. 3.5GPUs and accelerators
  6. 3.6Continuous integration of codes
  7. 3.7Visualisation with ParaView
  8. 3.8Workflow management
  9. 3.9Reproducibility
  10. 3.10Practical exercise: parallelisation of a diffusion solver in Python
  1. 4.1Finite difference discretisation
  2. 4.2Finite elements
  3. 4.3Finite volumes
  4. 4.4Iterative methods for linear systems
  5. 4.5Eigenvalue problems
  6. 4.6Newton-Krylov methods
  7. 4.7Time integration
  8. 4.8Stability and convergence
  9. 4.9Adaptive meshes
  10. 4.10Lab: finite element solver with FEniCS
  1. 5.1Neutron transport equation
  2. 5.2Diffusion approximation
  3. 5.3Discrete ordinates methods
  4. 5.4Method of characteristics
  5. 5.5Monte Carlo transport
  6. 5.6Lattice calculation and homogenisation
  7. 5.7Nodal core calculation
  8. 5.8Burn-up and isotopic evolution
  9. 5.9Serpent and OpenMC tools
  10. 5.10Practical exercise: lattice calculation with OpenMC
  1. 6.1Conservation equations
  2. 6.2Two-phase flow
  3. 6.3System codes RELAP5 and TRACE
  4. 6.4Subchannel codes COBRA-TF
  5. 6.5CFD in nuclear applications
  6. 6.6Heat transfer and boiling crisis
  7. 6.7Containment modelling
  8. 6.8Validation with experimental facilities
  9. 6.9Scaling
  10. 6.10Lab: loop model with RELAP5 or TRACE
  1. 7.1Process data acquisition
  2. 7.2Signal cleaning and validation
  3. 7.3Time series analysis
  4. 7.4Anomaly detection
  5. 7.5Data reconciliation
  6. 7.6Online sensor monitoring
  7. 7.7Plant databases
  8. 7.8Dashboards
  9. 7.9Data governance
  10. 7.10Practical exercise: sensor drift detection with Python
  1. 8.1Surrogate models of codes
  2. 8.2Physics-informed neural networks
  3. 8.3Reload optimisation with evolutionary algorithms
  4. 8.4Prediction of core parameters
  5. 8.5Transient diagnosis
  6. 8.6Explainability in safety applications
  7. 8.7Regulatory requirements for AI
  8. 8.8Datasets and benchmarks
  9. 8.9Deployment in industry
  10. 8.10Lab: surrogate model of a core parameter
  1. 9.1Sources of uncertainty
  2. 9.2Monte Carlo propagation
  3. 9.3Wilks' method
  4. 9.4Global sensitivity analysis
  5. 9.5BEPU methodology
  6. 9.6Perturbation methods
  7. 9.7DAKOTA and URANIE tools
  8. 9.8Uncertainty in nuclear data
  9. 9.9Communicating uncertainty
  10. 9.10Practical exercise: BEPU analysis with DAKOTA
  1. 10.1Code and solution verification
  2. 10.2Method of manufactured solutions
  3. 10.3Validation against experiments
  4. 10.4Validation matrices
  5. 10.5NQA-1 quality requirements
  6. 10.6Regulatory guidance on codes
  7. 10.7Code documentation
  8. 10.8Configuration management
  9. 10.9Software audits
  10. 10.10Workshop: V&V plan for a code
  1. 11.1MOOSE multiphysics platforms
  2. 11.2Code coupling
  3. 11.3High-fidelity simulation
  4. 11.4Exascale computing
  5. 11.5Reactor digital twins
  6. 11.6BISON fuel simulation
  7. 11.7Management of large simulation data
  8. 11.8International programmes NEAMS and CASL
  9. 11.9Future trends
  10. 11.10Case study: analysis of a neutronic-thermal coupling
  1. 12.1Definition of the simulation problem
  2. 12.2Selection of codes and data
  3. 12.3Model construction
  4. 12.4Model verification
  5. 12.5Validation against reference data
  6. 12.6Uncertainty quantification
  7. 12.7Analysis of results
  8. 12.8Conclusions
  9. 12.9Master's Thesis 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 OpenMC, Serpent, RELAP5, DAKOTA and MOOSE, in free, academic or evaluation versions. Real procedures are studied, such as neutronic lattice and core calculations, thermal-hydraulic system simulation and uncertainty analysis with the BEPU methodology. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ASME NQA-1, IAEA SSG-2, ENDF-6 Formats Manual, ASME V&V 20, IAEA SSR-2/1, 10 CFR 50 Appendix B, ISO/IEC 25010.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ASME NQA-1, IAEA SSG-2 and the ENDF-6 Formats Manual applied to real cases, not as a theoretical annex.
Industry software:OpenMC, Serpent, RELAP5, DAKOTA and MOOSE, in free, academic or evaluation versions.
Master's Thesis with defence:A complete simulation study, from code and data selection to model verification, validation and uncertainty quantification, 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.

Problem and codes
Define the simulation problem and select the codes and nuclear data.
Model construction
Build the neutronic or thermal-hydraulic model.
Verification and validation
Verify the model and validate it against reference data.
Uncertainty quantification
Quantify uncertainties and analyse the results.
Master's Thesis
Present the conclusions of the simulation study, defended before a panel.
Career opportunities

Where this takes you

Nuclear simulation engineerSafety analystScientific code developerPlant data scientistUncertainty quantification engineerMultiphysics researcher
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'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.

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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 OpenMC, Serpent, RELAP5, DAKOTA and MOOSE, 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's 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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