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