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CourseAerospace, UAM, Drones & Space Applications open

Course on Ethics of Robot-Collected Data

Online course of 300 h in Ethics of Robot-Collected Data. 6 modules covering Regulation (EU) 2016/679 (data protection, GDPR), Regulation (EU) 2023/2854 (Data Act) and ROS 2 (rosbag). Dedicated tutor and enrolment open.

Duration300 h
ModeOnline
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Identify the types of data collected by robots in nuclear environments.

02

Apply the GDPR to personal data captured by robotic systems.

03

Evaluate the quality and integrity of sensor and inspection data.

04

Analyse the applicable information security and cybersecurity requirements.

05

Define criteria for data ownership, sharing and access.

06

Assess the ethical implications of transparency in data use.

07

Design a data governance model for a robot fleet.

Profile

Who it's for

  • Robotics data engineer looking to specialise in Ethics of Robot-Collected Data.
  • Data protection officer looking to specialise in Ethics of Robot-Collected Data.
  • Industrial cybersecurity specialist looking to specialise in Ethics of Robot-Collected Data.
  • Data governance manager looking to specialise in Ethics of Robot-Collected Data.
  • Robotic inspection engineer looking to specialise in Ethics of Robot-Collected Data.
  • Information systems auditor looking to specialise in Ethics of Robot-Collected Data.
  • Engineering or science graduates looking to move into nuclear power generation.
Standards & tools

What you'll work with

  • Regulation (EU) 2016/679 (data protection, GDPR)
  • Regulation (EU) 2023/2854 (Data Act)
  • ISO/IEC 27001 (information security)
  • IEC 62645 (cybersecurity of nuclear instrumentation and control systems)
  • IAEA NSS 17 (computer security at nuclear facilities)
  • Directive (EU) 2022/2555 (NIS2)
  • ISO/IEC 25012 (data quality model)
Syllabus

6 modules · 300 h

  1. 1.1Data types: imaging, LiDAR, radiological and telemetry
  2. 1.2Radiological mapping with gamma sensors
  3. 1.3Digital twins fed by robots
  4. 1.4Inspection data and its regulatory value
  5. 1.5Incidentally captured personal data
  6. 1.6Metadata and acquisition traceability
  7. 1.7Storage in constrained environments
  8. 1.8The life cycle of robotic data
  9. 1.9Sensitive physical security information
  10. 1.10Case study: data inventory of a robotic mission
  1. 2.1The GDPR applied to robots in the workplace
  2. 2.2Legal bases for processing
  3. 2.3Privacy by design and by default
  4. 2.4Data protection impact assessment
  5. 2.5Worker monitoring by robots
  6. 2.6Facial recognition and biometric data
  7. 2.7Image minimisation and anonymisation
  8. 2.8Data subject rights
  9. 2.9International data transfers
  10. 2.10Workshop: DPIA of an inspection robot
  1. 3.1Quality of in-service inspection data
  2. 3.2ALCOA+ record integrity
  3. 3.3Sensor calibration and metrological traceability
  4. 3.4Biases and artefacts in captured data
  5. 3.5Training data for AI
  6. 3.6Data tampering and falsification
  7. 3.7Quality records according to IAEA GSR Part 2
  8. 3.8Digital signatures and chain of custody
  9. 3.9Data auditing and version control
  10. 3.10Practice: verifying the integrity of a dataset
  1. 4.1Cybersecurity of robots and connected systems
  2. 4.2IEC 62443 and industrial networks
  3. 4.3IAEA NSS 17 and NSS 33-T
  4. 4.4Sensitive information and classification
  5. 4.5Risks of plant data exfiltration
  6. 4.6Cloud security and data sovereignty
  7. 4.7Access control and segregation
  8. 4.8Security incident management
  9. 4.9Data and external suppliers
  10. 4.10Case study: threat model of a connected robot
  1. 5.1Data ownership: operator, manufacturer and researcher
  2. 5.2Data contracts and licences
  3. 5.3Sharing with regulators
  4. 5.4Open data and open science
  5. 5.5Data for public communication
  6. 5.6The EU Data Act and access
  7. 5.7Secondary use of data and consent
  8. 5.8The ethics of retraining models
  9. 5.9Long-term retention and archiving
  10. 5.10Workshop: a data-sharing agreement
  1. 6.1Defining the system and the data flow
  2. 6.2Data and risk classification
  3. 6.3Privacy and security assessment
  4. 6.4Integrity and quality requirements
  5. 6.5Sharing and access policy
  6. 6.6Retention and archiving plan
  7. 6.7Overall ethical assessment
  8. 6.8Summary of conclusions
  9. 6.9Report and presentation
  10. 6.10Final review with the tutor
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable materials, recorded classes and a dedicated technical tutor. No fixed timetable: students progress at their own pace within the intake period, making it easy to combine with work.

Each module combines theory, guided reading of the original standards and applied exercises. The software used is ROS 2 (rosbag), Python (pandas), PostgreSQL, Apache Kafka, in free, academic or trial versions. Testing is studied through real procedures from audit of an inspection robot's data flows, data protection impact assessment, integrity analysis of sensor logs. Assessment is continuous for each module, plus a final capstone project.

Accredited laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-driven Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:a safety-by-design approach, with cyber-OT, DIH and compliance as its pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Defining the system and the data flow

Identify the types of data collected by robots in nuclear environments.

Data and risk classification

Apply the GDPR to personal data captured by robotic systems.

Privacy and security assessment

Evaluate the quality and integrity of sensor and inspection data.

Integrity and quality requirements

Analyse the applicable information security and cybersecurity requirements.

Sharing and access policy

Define criteria for data ownership, sharing and access.

Career opportunities

Where this takes you

Robotics data engineerData protection officerIndustrial cybersecurity specialistData governance managerRobotic inspection engineerInformation systems auditor
Admissions & scholarships

Requirements

  • No previous qualification is required.
  • An internet connection and a computer able to run the programme software.
  • A level of 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 300 hours spread over 3 to 5 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.

You will work with ROS 2 (rosbag), Python (pandas), PostgreSQL, Apache Kafka. Free, academic or trial versions are used, and your tutor will point out a free alternative where one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the capstone project. It is SEIUM's own qualification, not an official state-accredited degree.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Check the recognition policy when you enrol.

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