Postgraduate Diploma in Clinical Validation and Data Privacy
Online 800-hour Postgraduate Diploma in Clinical Validation and Data Privacy. 8 modules with Regulation (EU) 2017/745 (MDR), ISO 14155:2020 and R. Assigned tutor and open enrolment.
Postgraduate Diploma in Clinical Validation and Data Privacy — studying from Malta
Malta has a well-developed telecommunications sector, with a history of hosting submarine cable landing points and a substantial base of network and communications providers. The island's regulatory environment supports digital infrastructure, and this has encouraged companies in embedded systems, network engineering and electronics design. Employers include telecommunications operators, software and hardware developers, and multinational technology firms with Maltese engineering teams. Engineers with knowledge of wireless systems, network security and embedded programming find steady demand.
Fees for Malta: €1,790 (Euro), with instalments and employer sponsorship available.
Relevant bodies in Malta
Typical roles
Questions from Malta
How are electronics and telecom engineers recognised professionally in Malta?
Engineering titles in Malta are regulated by the Chamber of Engineers (KSU) under the Engineering Profession Act. Telecommunications is overseen by the Malta Communications Authority, which regulates licensing and spectrum. A SEIUM qualification is a private one, not a state degree and not accredited by a national body, so recognition depends on employers and the relevant authority.
Which Maltese employers hire embedded systems and telecom engineers?
Malta has telecoms operators, electronics manufacturers and component suppliers, iGaming and technology firms that use embedded hardware, and defence and maritime electronics companies. Employers typically assess hands-on design experience and certifications. A private qualification can help your profile, especially alongside practical projects.
Can I fit this programme around work, and what are the practical requirements?
The programme is 100% online and taught in English, with an assigned technical tutor. You can study from Malta without a student visa. Choose a course length that matches your available time, from 300 hours up to 1,900. Ask about fee instalments and employer sponsorship before enrolling.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Classify software medical devices under the MDR.
Plan evidence-based clinical evaluations.
Design clinical investigations in accordance with ISO 14155.
Statistically analyse diagnostic performance.
Apply the GDPR to the processing of health data.
Implement privacy-by-design techniques.
Integrate cybersecurity and risk management into the technical file.
Who it's for
- Regulatory affairs specialist who wants to specialise in Clinical Validation and Data Privacy.
- Clinical research manager who wants to specialise in Clinical Validation and Data Privacy.
- Healthcare data protection officer who wants to specialise in Clinical Validation and Data Privacy.
- Biostatistician who wants to specialise in Clinical Validation and Data Privacy.
- Medical device quality engineer who wants to specialise in Clinical Validation and Data Privacy.
- Clinical evaluation manager who wants to specialise in Clinical Validation and Data Privacy.
- Engineering or science graduates looking to enter health technology.
What you'll work with
- Regulation (EU) 2017/745 (MDR)
- ISO 14155:2020
- Regulation (EU) 2016/679 (GDPR)
- IEC 62304:2006+A1:2015
- ISO 14971:2019
- ISO 13485:2016
- IEC 81001-5-1:2021
- Organic Law 3/2018
8 modules · 800 h
1.1Definition of a medical device1.2Software as a Medical Device (SaMD)1.3MDR classification rules1.4Rule 11 for software1.5General safety and performance requirements1.6Notified bodies and CE marking1.7FDA framework and the 510(k) pathway1.8In vitro diagnostic medical devices1.9IEC 62304 lifecycle1.10Workshop: classification of a monitoring app
2.1Clinical evaluation plan2.2Equivalence and literature data2.3Systematic review of the evidence2.4Scientific validity, technical and clinical performance2.5Clinical evaluation of software per MDCG 2020-12.6Clinical evaluation report2.7Post-market clinical follow-up2.8Benefit-risk and state of the art2.9Updating the evaluation2.10Case study: clinical evaluation plan for a SaMD
3.1Design of validation studies3.2ISO 14155 and good clinical practice3.3Protocol and inclusion criteria3.4Sample size calculation3.5Ethics committees and authorisation3.6Informed consent and participant information sheet3.7Monitoring and adverse event management3.8Clinical data management3.9Final investigation report3.10Practice: drafting a study protocol
4.1Sensitivity, specificity and predictive values4.2ROC curves and AUC4.3Agreement and Bland-Altman4.4Kappa index and reliability4.5Confidence intervals4.6Non-inferiority tests4.7Selection and spectrum bias4.8Validation of AI algorithms4.9Reporting per STARD and TRIPOD4.10Laboratory: diagnostic performance analysis in R
5.1GDPR and special categories of data5.2Legal bases for processing5.3Research and secondary use of data5.4Data protection impact assessment (DPIA)5.5Data protection officer5.6International transfers of clinical data5.7European Health Data Space5.8Organic Law 3/2018 and health5.9HIPAA in US contexts5.10Case study: DPIA for a remote monitoring platform
6.1Principles of privacy by design6.2Data minimisation and purpose limitation6.3Pseudonymisation and anonymisation6.4Re-identification risk6.5k-anonymity and differential privacy6.6Federated learning in healthcare6.7Encryption and key management6.8Access control and auditing6.9Consent management6.10Practice: pseudonymising a clinical dataset
7.1Cybersecurity per MDCG 2019-167.2IEC 81001-5-1 for health software7.3Clinical risk management with ISO 149717.4ISO 13485 quality management system7.5Usability per IEC 62366-17.6Vigilance and incident reporting7.7Change management for validated software7.8HL7 FHIR interoperability7.9Notified body audits7.10Laboratory: threat analysis of a SaMD
8.1Description of the device and its intended purpose8.2Classification and regulatory pathway8.3Summary of the clinical evaluation plan8.4Validation protocol8.5Statistical analysis plan8.6DPIA and privacy measures8.7Risk management and cybersecurity8.8Summary technical documentation8.9Report and presentation8.10Final review with the tutor
How you'll learn
Fully 100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the programme with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is R, REDCap, ARX Data Anonymization Tool, Python with scikit-learn, OpenClinica, in free, academic or evaluation versions. Case studies are based on real procedures from diagnostic performance validation studies, data protection impact assessments and re-identification risk analyses. Assessment is continuous, module by module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Classify software medical devices under the MDR.
Plan evidence-based clinical evaluations.
Design clinical investigations in accordance with ISO 14155.
Statistically analyse diagnostic performance.
Apply the GDPR to the processing of health data.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient English to follow technical regulatory texts.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 800 hours, spread over 6 to 8 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.
The programme uses R, REDCap, ARX Data Anonymization Tool, Python with scikit-learn and OpenClinica. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An own diploma from SEIUM Institute on passing the continuous assessment and the integrative project. This is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or diploma in the same area are recognised towards the advanced programme. Please check the recognition policy when you enrol.
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