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Master'sRobotics, Automation & Control Applications open

Master’s in Edge Computing for OT Engineering

Online master's programme of 1,900 hours in edge computing engineering for OT, structured in 12 modules covering edge hardware and operating systems, containers and orchestration, plant data acquisition, stream processing, edge AI, virtualised control, edge-cloud integration, OT edge cybersecurity and fleet operations, 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 edge computing architectures for OT.

02

Select edge hardware and operating systems.

03

Deploy industrial applications in containers.

04

Acquire and contextualise plant data at the edge.

05

Run analytics and artificial intelligence at the edge.

06

Secure and operate fleets of edge devices.

07

Integrate the edge with cloud platforms.

Profile

Who it's for

  • Edge computing architects designing industrial platforms.
  • OT integration engineers connecting PLCs and plant systems to new data layers.
  • Industrial DevOps engineers deploying containers on the plant floor.
  • IIoT specialists building gateway-based solutions.
  • Plant data engineers working on real-time analytics.
  • Digital transformation leads driving Industry 4.0 projects.
  • Engineering or science graduates seeking to enter discrete manufacturing.
Standards & tools

What you'll work with

  • IEC 62443-4-2:2019 (security requirements for IACS components)
  • IEC 62443-3-3:2013 (system security requirements and security levels)
  • IEC 61499-1:2012 (function blocks for distributed control)
  • IEC 62541-1:2020 (OPC Unified Architecture — overview and concepts)
  • Regulation (EU) 2024/2847 (Cyber Resilience Act)
  • IEC 62264-1:2013 (enterprise-control system integration, ISA-95 models)
  • ISO/IEC 30141:2024 (Internet of Things reference architecture)
  • Docker
  • K3s
  • Node-RED
  • Azure IoT Edge
  • AWS IoT Greengrass
Syllabus

12 modules · 1900 h

  1. 1.1. Differences between IT and OT at the edge
  2. 1.2. Industrial edge use cases
  3. 1.3. Edge, fog and cloud architectures
  4. 1.4. Latency and determinism requirements
  5. 1.5. Availability and service life on the plant floor
  6. 1.6. Fanless industrial hardware
  7. 1.7. Industrial Internet Consortium reference models
  8. 1.8. Total cost of an edge platform
  9. 1.9. Edge vendors and ecosystems
  10. 1.10. Case study: identifying edge use cases
  1. 2.1. Industrial PCs and IoT gateways
  2. 2.2. ARM and x86 processors on the plant floor
  3. 2.3. Embedded AI accelerators
  4. 2.4. Industrial Linux and the Yocto Project
  5. 2.5. Linux with the PREEMPT_RT patch
  6. 2.6. Real-time hypervisors
  7. 2.7. Secure boot and TPM
  8. 2.8. Robust storage and wear
  9. 2.9. Industrial hardware certifications
  10. 2.10. Lab: latency measurement with PREEMPT_RT
  1. 3.1. Docker for industrial applications
  2. 3.2. Lightweight and multi-architecture images
  3. 3.3. Lightweight Kubernetes with K3s
  4. 3.4. Orchestration across device fleets
  5. 3.5. Resource and priority management
  6. 3.6. Containers with real-time requirements
  7. 3.7. Private image registries
  8. 3.8. GitOps deployments at the edge
  9. 3.9. Zero-downtime updates
  10. 3.10. Practical exercise: deploying K3s on a gateway
  1. 4.1. Connecting to PLCs via OPC UA
  2. 4.2. S7, Modbus and EtherNet/IP drivers
  3. 4.3. Data normalisation and contextualisation
  4. 4.4. Data flows with Node-RED
  5. 4.5. Buffering against connection loss
  6. 4.6. Timestamps and quality
  7. 4.7. Local edge historians
  8. 4.8. Asset modelling at the edge
  9. 4.9. Publishing to a Unified Namespace
  10. 4.10. Lab: acquisition flow in Node-RED
  1. 5.1. Complex event processing
  2. 5.2. Time windows and aggregation
  3. 5.3. Real-time KPI calculation
  4. 5.4. Anomaly detection in streaming data
  5. 5.5. Data filtering and compression
  6. 5.6. Industrial rule engines
  7. 5.7. Local time-series databases
  8. 5.8. Reducing traffic to the cloud
  9. 5.9. Consistency of distributed data
  10. 5.10. Practical exercise: OEE calculation at the edge
  1. 6.1. Model inference on gateways
  2. 6.2. Predictive maintenance with vibration data
  3. 6.3. Distributed machine vision
  4. 6.4. ONNX models and acceleration
  5. 6.5. Cloud training and local inference
  6. 6.6. Industrial federated learning
  7. 6.7. Model drift monitoring
  8. 6.8. Memory and compute constraints
  9. 6.9. Model life-cycle management
  10. 6.10. Workshop: anomaly model deployed on a gateway
  1. 7.1. Virtual PLCs and software-defined automation
  2. 7.2. Control in deterministic containers
  3. 7.3. Separation of critical workloads
  4. 7.4. Distributed IEC 61499 architectures
  5. 7.5. Redundancy of virtual controllers
  6. 7.6. Network and control latencies
  7. 7.7. Integration with TSN
  8. 7.8. Risks and limits of virtual control
  9. 7.9. Emerging industrial cases
  10. 7.10. Case study: evaluating a virtual PLC
  1. 8.1. AWS IoT Greengrass and Azure IoT Edge platforms
  2. 8.2. Bidirectional data synchronisation
  3. 8.3. Remote device management
  4. 8.4. Telemetry and commands
  5. 8.5. Industrial data lakes
  6. 8.6. Hybrid processing models
  7. 8.7. Transfer and storage costs
  8. 8.8. Data sovereignty and Gaia-X
  9. 8.9. Multi-cloud and hybrid architectures
  10. 8.10. Practical exercise: sending telemetry to the cloud
  1. 9.1. The edge attack surface
  2. 9.2. IEC 62443-4-2 requirements
  3. 9.3. Hardening Linux systems
  4. 9.4. Identity and certificate management
  5. 9.5. Image signing and the supply chain
  6. 9.6. IT/OT segmentation
  7. 9.7. Security monitoring
  8. 9.8. Incident response on devices
  9. 9.9. The European Cyber Resilience Act
  10. 9.10. Case study: hardening a gateway
  1. 10.1. Zero-touch device provisioning
  2. 10.2. Inventory and configuration management
  3. 10.3. Secure OTA updates
  4. 10.4. Observability with Prometheus
  5. 10.5. Centralised logging
  6. 10.6. Device alerts and SLAs
  7. 10.7. Remote incident management
  8. 10.8. Hardware life cycle
  9. 10.9. Scaling to multiple plants
  10. 10.10. Workshop: monitoring with Prometheus and Grafana
  1. 11.1. Capturing OT requirements
  2. 11.2. Platform selection
  3. 11.3. Defining your own reference architecture
  4. 11.4. Sizing compute and storage
  5. 11.5. Availability plan
  6. 11.6. Risk analysis
  7. 11.7. Proof of concept and pilot
  8. 11.8. Return on investment
  9. 11.9. Organisational change management
  10. 11.10. Case study: edge pilot on a production line
  1. 12.1. Definition of the OT use case
  2. 12.2. Proposed edge architecture
  3. 12.3. Data acquisition and modelling
  4. 12.4. Analytics or AI at the edge
  5. 12.5. Cloud integration
  6. 12.6. Security and fleet operation
  7. 12.7. Pilot testing and results
  8. 12.8. Conclusions and scaling
  9. 12.9. Master's Thesis report
  10. 12.10. Master'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 Docker, K3s, Node-RED, Azure IoT Edge and AWS IoT Greengrass, in free, academic or evaluation versions. Exercises are based on real procedures for deploying containers on industrial gateways, real-time analytics of plant data and hardening edge devices. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: IEC 62443-4-2:2019, IEC 62443-3-3:2013, IEC 61499-1:2012, IEC 62541-1:2020, Regulation (EU) 2024/2847, IEC 62264-1:2013, ISO/IEC 30141:2024.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of IEC 62443, IEC 61499 and the Cyber Resilience Act applied to real cases, not as a theoretical annex.
Industry software:Docker, K3s, Node-RED, Azure IoT Edge and AWS IoT Greengrass, in free, academic or evaluation versions.
Master's Thesis with defence:An edge computing pilot for a real OT use case, 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 OT use case
Identify an edge use case on a production line and capture its requirements.
Design the edge architecture
Select hardware, operating system and container orchestration for the solution.
Acquire and process plant data
Build acquisition flows and real-time analytics or AI models at the edge.
Secure and operate the fleet
Harden devices, integrate with the cloud and set up monitoring and OTA updates.
Master's Thesis
An edge computing pilot for a real OT use case, defended before a panel.
Career opportunities

Where this takes you

Edge computing architectOT integration engineerIndustrial DevOps engineerIIoT specialistPlant data engineerDigital transformation lead
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 Docker, K3s, Node-RED, Azure IoT Edge and AWS IoT Greengrass, 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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