Postgraduate Programme Specification
PhD Future Open SecuRe NeTworks (FORT)
Academic Year 2024/25
A programme specification is required for any programme on which a student may be registered. All programmes of the University are subject to the University's Quality Assurance processes. All degrees are awarded by Queen's University Belfast.
| Programme Title | PhD Future Open SecuRe NeTworks (FORT) | Final Award (exit route if applicable for Postgraduate Taught Programmes) |
Doctor of Philosophy | |||||||||||
| Programme Code | FORT-PHD | UCAS Code | HECoS Code |
100163 - Electrical and electronic engi - 50 100366 - Computer science - 50 |
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ATAS Clearance Required |
No |
Health Check Required |
No |
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Portfolio Required |
-- |
Interview Required |
Interviews arranged by each institution with common scoring template agreed. |
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| Mode of Study | Full Time | |||||||||||||
| Type of Programme | Research | Length of Programme |
Full Time - 4 Academic Years |
Total Credits for Programme | 120 | |||||||||
| Exit Awards available | No | |||||||||||||
Institute Information
Teaching Institution |
Queen's University Belfast |
School/Department |
Electronics, Electrical Engineering & Computer Science |
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Quality Code Higher Education Credit Framework for England |
Level 8 |
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Subject Benchmark Statements The Frameworks for Higher Education Qualifications of UK Degree-Awarding Bodies |
Computing (2022) |
Accreditations (PSRB) |
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No accreditations (PSRB) found. | |
Regulation Information
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Does the Programme have any approved exemptions from the University General Regulations |
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Programme Specific Regulations This is a structured four-year, full-time research programme with a taught element in the first year and research in the following years (2, 3 and 4) with each stage subject to Study Regulations for Postgraduate Taught programmes and Research Degree Programmes as per the University’s Principles for Professional Doctorates and Integrated PhDs. |
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Students with protected characteristics |
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Are students subject to Fitness to Practise Regulations (Please see General Regulations) No |
Educational Aims Of Programme
The CDT-FORT training approach will deliver an enhanced doctoral degree that combines extensive
deep technical knowledge in a specific domain area with an appreciation of a range of topics so that
graduates will understand the broader context of their research and have the necessary skills to
transition smoothly into industry or academia. CDT-FORT’s specific learning objectives have been
brought together in discussion with our stakeholders as follows:
1.To produce a cohort of industry-conscious thinkers and leaders with a unique range of expertise across wireless communications, cybersecurity, networking and AI, who will influence the roadmaps of future open networking and security technologies and applications.
2.To enable PGRs to address challenges that are strongly relevant to industry and society by upskilling them and providing a supportive, collaborative environment with close interaction between all stakeholders.
3.To develop innovations that address the complex challenges involved in delivering future, open, secure and resilient communications systems, ensuring that the networks are secure by design and fit for the future and in support of critical national infrastructure.
4.To conduct responsible research and effective innovation in the field by encouraging ethical and society-focused exploration of emerging cyber security and AI technologies, future networks and their challenges.
5.To produce world leading scholarly output and impact.
Learning Outcomes
Learning Outcomes: Cognitive SkillsOn the completion of this course successful students will be able to: |
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The creation and interpretation of new knowledge, through original research or other advanced scholarship, of a quality to satisfy peer review, extend the forefront of the discipline, and merit publication. |
Teaching/Learning Methods and Strategies Learning through interaction with supervisors, experts in the discipline, or communication with peers. Learning by doing, reading and thinking Methods of Assessment Initial Review, differentiation, APRs and PhD Viva |
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The general ability to conceptualise, design and implement a project for the generation of new knowledge, applications or understanding at the forefront of the discipline, and to adjust the project design in the light of unforeseen problems. |
Teaching/Learning Methods and Strategies Learning through interaction with supervisors, experts in the discipline, or communication with peers. Learning by doing, reading and thinking Methods of Assessment Initial Review, differentiation, APRs and PhD Viva |
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The ability to make informed judgements on complex issues in specialist fields, often in the absence of complete data, and be able to communicate their ideas and conclusions clearly and effectively to specialist and non-specialist audiences. |
Teaching/Learning Methods and Strategies Learning through interaction with supervisors, experts in the discipline, or communication with peers. Learning by doing, reading and thinking Methods of Assessment Initial Review, differentiation, APRs and PhD Viva |
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Continue to undertake pure and/or applied research and development at an advanced level, contributing substantially to the development of new techniques, ideas, or approaches |
Teaching/Learning Methods and Strategies Learning through interaction with supervisors, experts in the discipline, or communication with peers. Learning by doing, reading and thinking Methods of Assessment Initial Review, differentiation, APRs and PhD Viva |
Learning Outcomes: Knowledge & UnderstandingOn the completion of this course successful students will be able to: |
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A systematic acquisition and understanding of a substantial body of knowledge which is at the forefront of an academic discipline or area of professional practice. |
Teaching/Learning Methods and Strategies Taught Modules in Year 1. Learning through interaction with supervisors, experts in the discipline, or communication with peers. Learning by doing, reading and thinking Methods of Assessment Module assessments, Year 1 progression review, Initial Review, differentiation, APRs and PhD Viva |
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A detailed understanding of applicable techniques for research and advanced academic enquiry |
Teaching/Learning Methods and Strategies Learning through interaction with supervisors, experts in the discipline, or communication with peers. Learning by doing, reading and thinking Methods of Assessment Module assessments, Year 1 progression review, Initial Review, differentiation, APRs and PhD Viva |
Learning Outcomes: Subject SpecificOn the completion of this course successful students will be able to: |
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Have a technical competency in AI, Comms and Cyber Security, as appropriate for their corresponding research project |
Teaching/Learning Methods and Strategies Taught Modules in Year 1 Methods of Assessment Module assessments, Year 1 progression review, Initial Review, differentiation, APRs and PhD Viva |
Learning Outcomes: Transferable SkillsOn the completion of this course successful students will be able to: |
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The qualities and transferable skills necessary for employment requiring the exercise of personal responsibility and largely autonomous initiative in complex and unpredictable situations, in professional or equivalent environments. |
Teaching/Learning Methods and Strategies Transferable skills training will be delivered in person (where possible) to assist with cohort integration by QUB Innovation staff, Graduate school and external trainers Methods of Assessment Differentiation, APRs and PhD Viva |
Module Information
Stages and Modules
| Module Title | Module Code | Level/ stage | Credits | Availability |
Duration | Pre-requisite | Assessment |
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|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S1 | S2 | Core | Option | Coursework % | Practical % | Examination % | ||||||
| Cyber - AI | ELE8100 | 8 | 20 | -- | YES | 12 weeks | N | -- | YES | 100% | 0% | 0% |
| Software Assurance | ELE8094 | 8 | 20 | -- | YES | 12 weeks | N | -- | YES | 100% | 0% | 0% |
| Data, Privacy and the Law | LAW7848 | 8 | 10 | YES | -- | 12 weeks | N | YES | -- | 100% | 0% | 0% |
| Machine Learning | ECS8051 | 8 | 20 | YES | -- | 12 weeks | N | YES | -- | 100% | 0% | 0% |
| Cryptography | ELE8090 | 8 | 20 | -- | YES | 12 weeks | N | -- | YES | 100% | 0% | 0% |
| Network Security and Monitoring | ELE8093 | 8 | 20 | YES | -- | 12 weeks | N | -- | YES | 100% | 0% | 0% |
| Foundations of Cyber Security | ELE8071 | 8 | 10 | YES | -- | 12 weeks | N | YES | -- | 100% | 0% | 0% |
Notes
No notes found.