This programme is designed as a specialised extension to the study of Electronics at undergraduate level. The programme provides students with the opportunity to deepen their understanding and develop specialist expertise in a range of advanced electronics subjects including microelectronics, sensors, signal processing, hardware and software design, communications, and digital systems.
PLEASE NOTE:
Applications for this course received after 30th June 2025 may not be accepted. In addition, a deposit will be required to secure a place.
Electronics highlights
World Class Facilities
Our facilities have recently undergone a £10m refurbishment and include laboratories for Microengineering, Electronics, Communications, Circuits, Instrumentation, Virtual Reality, Software Engineering, Renewable Energy, Power and Machines. The Queen's Advanced Micro-engineering Centre (QAMEC) is a Centre of Excellence for research and development employing silicon technology and MEMS technology.
Internationally Renowned Experts
An example of our research includes our work in the area of space technologies, where we are involved in a number of projects with the European Space Agency, the new UK Space Applications Catapult Centre, the European High Power Radio-Frequency Space Laboratory and companies such as Astrium, Thales and QinetiQ.
Professional Accreditations
MSc in Electronics is seeking to update its accreditation by the Institution of Engineering and Technology (IET) on behalf of the Engineering Council as meeting the requirements for Further Learning for registration as a Chartered Engineer. Candidates must hold a CEng accredited BEng/BSc (Hons) undergraduate first degree to comply with full CEng registration requirements.
Students may enrol on a full-time (1 year) or part-time (2 or 3 years) basis. Taught modules are delivered over two semesters.
Full-time students follow taught modules in Semester 1 (Autumn) and Semester 2 (Spring) and they carry out an independent research project and write their MSc thesis (dissertation) in the summer.
Part-time students may take the course over a two or three year period and are required to take at least two modules per year.
An MSc is awarded to candidates who pass six taught modules (120 CATS points) and the individual research project (60 CATS points).
An “MSc with Professional Placement” is awarded to those students who meet these requirements and are able to secure a (minimum) 9-month placement in an appropriate industrial sector (to be approved by the school).
Two other exit qualifications exist: (i) A Postgraduate Diploma is awarded to students who will pass six modules (120 CATS points) and (ii) a Postgraduate Certificate is awarded to students who pass three modules (60 CATS points).
Course Details
The MSc consists of a practical project of a research nature (60 CATS) plus six modules (120 CATS). The PGDip consists of six modules (120 CATS). Modules normally run from September until June, with the project commencing in November and running until the following September.
In any given year further specialist topics may be available or some topics may not be offered.
Programme Modules
This MSc programme in Electronics is designed to train the next generation of Electrical and Electronic Engineers who will have the necessary skills to occupy prestigious positions in the industry or research institutes and the academia. The curriculum involves the following taught modules (the first two modules are compulsory and you need to choose another four):
Note, we constantly review the syllabus to ensure we are up to date and industry relevant, therefore modules are subject to change and not all modules are guaranteed to be offered each year.
MEMS Devices and Technology: Microelectromechanical devices (MEMS devices) are increasingly common in a wide range of applications, e.g. environmental and biomedical sensing, automotive systems and portable electronics. This module will examine the structure and operation of a range of common MEMS devices including pressure sensors and accelerometers. The design of such devices will be explored, as will a range of sensing and actuation methods. The module will include fabrication technology for silicon-based MEMS including details of processes such as material deposition, etching, and wafer bonding.
Microelectronics Devices and Technology: This module offers a detailed discussion of the fabrication and internal electronics of modern silicon devices. Bipolar and MOS technologies are covered from first principles, such that students should be able to apply their learning to all silicon-based devices. You will be introduced to the realities of present-day scaling and the key parameters which control device performance.
Intelligent Systems and Control: Intelligent Systems and Control develops a robust understanding of the major academic topics which define control methods and intelligent algorithms in dynamic systems. Special focus is given on analysis and design methodologies for control systems alongside an introduction to artificial intelligence.
Wireless Communications: This module provides the concepts and techniques required for the generic design of modern wireless communication systems. Wireless communication systems are emerging as a primary enabling technology in the realisation of smarter connected devices in the future digital society. The module will focus on the fundamentals of wireless system design and include a study of progressive trends in communications and challenges posed by the next generation wireless systems.
Wireless Sensor Systems: This module gives an introduction to Wireless Sensor Networks, their capabilities, applications, the Internet of Things (IoT) concept, enabling technologies and standards. It includes collaborative sensing, aggregation of data, data analysis, communication protocols, MAC-layer, routing protocols, energy-aware operation, power management, time synchronization and synchronization protocols.
Control and Estimation Theory: You will learn how to design a stabilising model predictive controller (MPC), which is an advanced control methodology used in modern control applications (robotics, process industry, aerospace, automotive, etc). We will also give an introduction to probability and statistics and learn the basics of estimation theory, with special focus on the Kalman filter and nonlinear state estimation methodologies. The module includes lab sessions where the students acquire hands-on experience in the applications of control and estimation theory.
Digital Signal Processing: This module covers a number of key topics on digital signal processing and its applications. Particular topics include, but are not limited to, Fourier series, Fourier and Laplace transforms, Sampling, Analog and Digital filter design, Adaptive Filters and the celebrated Discrete and Fast Fourier Transforms.
Each student needs to carry out an individual MSc project under the supervision of an academic. We offer a very large number of project proposals every year. Some of our top students publish the results of their work in esteemed international peer-reviewed journals and conferences.
Note that the above taught modules will be offered conditional on having an adequate number of enrolled students. If you would like further information, do not hesitate to contact the course director, Dr Pantelis Sopasakis at p.sopasakis@qub.ac.uk.
You will be taught by a team of experts in their subject areas and are active researchers in those subjects. Often our students conduct their individual research project within an ongoing research project so they get exposed to the state of the art in electrical and electronics engineering.
English Language Support
The school is offering support on the use of English in academic writing. This will help you not only during your studies at Queen’s, but also in your future career.
Mathematics Support
The school is offering additional support on mathematics. Queen’s University is also home to MASH: the Mathematics and Statistics Helpdesk.
Modules
Each module normally involves two hours of lectures per week. Some modules include additional tutorial sessions (where you solve exercises or address practical problems with the help of your lecturer) or laboratory exercises
Transferable Skills
We give a lot of emphasis on the development of transferable skills, such as the communication skills, time management and prioritisation, research and critical thinking, effective CV writing and interview skills, collaboration and more.
Virtual Learning Environment
All modules have a virtual learning environment (using Canvas) where the students can find all relevant material (lecture notes, handouts, video lectures) as well as online quizzes and assignments. Without a doubt, having all learning resources in one place is very useful.
Assessment
Assessments associated with the course are outlined below:
Coursework
Written examination
Project dissertation
What our academics say
The MSc Electronics programme offers an advanced study pathway to develop graduate engineers with the relevant knowledge, skills and professional competencies necessary for employment in technical development, operational analysis, managerial and senior technical positions such as Chief Engineer, or as preparation for further research - particularly at doctoral level.
Dr Pantelis Sopasakis
Course Director, Senior Lecturer, MSc Electronics
The information provided in this Course Finder reflects the module details for the current year of study (2025/26). Please note that modules are subject to annual review and changes may occur in response to various factors, including student feedback and academic developments. Prospective students will be notified of any significant changes to module offerings before the start of the new academic year.
Signals and spectral representation, linear systems, Fourier and Laplace transform, convolution, impulse response, transfer function, sampled data, sampling theorem, design of analogue filters, infinite impulse response (IIR) filters, finite impulse response (FIR) filters, truncation and windowing. Decimation, interpolation, multi-rate processing. Discrete Fourier transform (DFT), fast Fourier transform (FFT), spectral analysis, FFT applications. Estimation theory, the Wiener filter, adaptive algorithms, recursive least squares, stochastic gradient algorithms.
Coursework:
1. Assignment 1: problem set (theory and practical Matlab elements)
2. Assignment 2: Digital filter design for noise removal from ECG signal (design in Matlab, submission via report)
Learning Outcomes
• Application of elementary algebra, complex number theory, linear algebra, statistics, and calculus in the derivation and analysis of signal processing systems and algorithms.
• Digital IIR and FIR ideal filter design requires derivation (applying various maths techniques) of coefficients from given filter requirement data, considering application-related constraints on the global filter characteristics; the principles underlying the advantages and limitations of different approaches (e.g., Butterworth, Chebyshev, FIR linear-phase etc) need to be understood and applied. The derivation, choice (e.g., LMS vs RLS) and application of optimal and adaptive filtering techniques requires analysis and characterisation of the signal statistics as determined by the application scenario.
• Selecting appropriate techniques for calculation of convolution output; choice of appropriate window in DFT analysis; selection of filter type / mapping on the basis of given application criteria & requirements; selection of adaptive algorithm in adaptive filtering applications.
• CW2 involves communicating via a technical report the specific features, effectiveness, and limitations of the taken approach to address the ECG signal noise reduction problem.
• In most weeks, the tutorial questions require to addressing a problem both with theory and by validation in software.
• In CW2, the students study digital filter design independently, from a variety of sources and by a variety of techniques.
• Throughout the course, students design and write code for a wide range of signal processing algorithms
• CW1 and CW2 require students to manage their own learning and development including time management and organisational skills.
• In CW2, students need to articulate and effectively communicate the design and technological rationale for a chosen digital filter design in their technical reports.
Skills
• numeric
• problem solving
• design, implement and test digital filter designs in Matlab
• perform spectral analysis on signals
Wireless communications are now part of everyday life, enabling systems and networks such as 5G, WiFi and the internet of things to name but a few. This module develops the necessary concepts required to understand the technology behind present-day wireless systems and sensors. It also explores some of the technologies which are likely to underpin future wireless systems such as MIMO and OFDM. Among the topics that will be covered are:
• Physical wireless layer technology, transmitter and receiver architectures
• Antennas and their parameters (gain, bandwidth, radiation pattern, efficiency, losses, sidelobes)
• Modulation and demodulation
• Signal-to-noise ratio (SNR) and Friis Law in high frequency circuit designs
• Frequency selective and flat fading channels, Rayleigh and Rician Fading models
• Multi-antenna diversity (Selection Combining, Equal Gain Combining, Maximal ratio Combining)
• Multiple Input Multiple Output Systems
• Orthogonal Frequency Division Multiplexing
Coursework 1:
1. Focuses on radiofrequency system design and analyses of transmitter and receiver architectures in wireless systems.
2. Analyses of wireless networks, including SNR, antennas, system noise figure will be performed
3. Modulation and demodulation, and relevant techniques to realize them will be performed
Coursework 2:
1. Focuses on MIMO and Multi-Antenna Diversity Strategy namely selection combining, equal gain combining and maximal ratio combining techniques, to enhance signal robustness in wireless communications.
2. Applies these concepts to evaluate how varying the number of diversity branches affects key performance metrics (such as bit error rate and outage probability) in wireless systems.
3. Investigates the principles underlying the MMSE receiver by analysing its performance across different SNR regimes (low and high SNR regions).
Additional Resources and Recommended literature:
The lecture materials are provided on CANVAS on a weekly-basis, and they are self-sufficient. Additional references & recommended literature for further information include:
C.A. Balanis, Antenna Theory: Analysis and Design, John Wiley & Sons, 2015
T.S. Rappaprt, Wireless Communications: Principles and Practice, Cambridge University Press, 2024
Simon Haykin & Mathini Sellathurai, Elements of Digital Comms, Wiley
Simon Haykin & M. Moher, Digital and Analogue Communications
Gordon L. Stuber, Principles of Mobile Communication
Learning Outcomes
Science and Mathematics:
• Demonstrate a good understanding of the physical hardware technology for radio wave propagation, including transmitter and receiver architectures, and the associated antenna architectures and modulation techniques forming wireless systems. Learn the concept of Friis Law and its application to high-frequency wireless circuits.
• Understand fading and the need for statistical approaches to modelling signal propagation and reception in wireless systems. Understand fundamental wireless communication concepts that are being used in current and future wireless systems.
Problem Analysis:
• Apply noise theory and Friis Law to design, analyse and optimize transmitter and receiver systems in wireless communications.
• Use learning from the Gaussian statistics as well as the Rayleigh and Rician fading models, to work with more advanced (unseen) fading models. Use this knowledge to determine different performance measures related to wireless communications.
Analytical Tools and Techniques:
• Mathematical analysis and theoretical modelling are employed to derive and evaluate performance metrics such as outage probability and bit error rate for multi-antenna diversity systems, including the derivation of the optimal MMSE receiver
Skills
Assimilation of lecture material, python skills, system model and problem-solving skills as well the application of probability, statistics, electromagnetic theory, and time-series forecasting to wireless data sets.
The module covers key building blocks and essentials in wireless sensor systems. The lecture notes cover mainly protocols at layer 2 (MAC layer) and layer 3 (routing) as well as key technologies for enabling IoT (ZigBee, 6LoWPAN, LoRaWAN, 802.11ah). Power management in WSN, synchronization and synchronization protocols are also covered.
The coursework covers another two key aspects: sensor technology (CW1) and analysing and forecasting sensor data (CW2 and CW3).
• Apply knowledge of mathematics for: Throughput and delay calculations. Time synchronization. Power consumption calculations.
• Apply knowledge of statistics to a broadly define problem (sensor data sets and time series from pollutant data in Coursework 2)
• Analysing the suitability of existing wireless technologies (which may include ZigBee, WiFi, IEEE 802.11ah, LoRaWAN) to support Internet of Things. Limitations of each technology. Appreciation of new developments in IoT (systems and platforms).
• Analyse broadly defined problems reaching conclusions. Conclusions on data patterns, data trends and forecasting in Coursework 3.
• Selecting and applying techniques such as regression for forecasting in Coursework 3 using appropriate software. Recognising limitations on forecasting and measuring performance.
• Select and evaluate technical literature for assessing, comparing, and choosing a specific sensor in the marketplace as part of Coursework 1.
• Understanding sensors to monitor air pollution and how data analysis and forecasting can aid in predicting pollutant concentrations.
• Understanding of different roles in a collaborative project in coursework 3. Initiative and personal responsibility for their individual role.
• Apply an integrated system approach in wireless sensor systems throughout the lectures. Understanding different sub-systems and interfaces in a sensor systems and being able to understand how these are put together for different applications.
• Understanding of telecommunications protocols used for communication in the system. Understanding enabling technologies for the Internet of Things. Recent standardization activity on these new technologies.
• Data Analytics Skills as part of Coursework 3. Use of statistics, forecasting and appropriate software.
• Select and apply appropriate sensors in Coursework 1 and engineering technologies for enabling Sensor networks and IoT throughout the lectures.
• Project management in teams for coursework 3 (data analytics project), commercial context in coursework 1 (researching and choosing a commercial sensor to measure air pollution).
Skills
The ability to critically assess and design modern wireless communications systems and in particular wireless sensor networks and systems.
The ability to understand existing sensors, system architectures, communication protocols and standards in such a context.
Use software, statistics and mathematical techniques for sensor data analysis and forecasting.
Control theory and engineering is at the core of robotics applications, with recent technological advances extending the boundaries of these fields towards learning and in general AI methods. This course will introduce and provide algorithmic tools from modern control theory, specifically state space analysis, focusing on control, estimation and system identification.
In the first part of the course we introduce the state space formalism for modelling and analysing dynamical systems, simulation tools for general nonlinear systems, and provide an introduction to other modelling formalisms (e.g., hybrid systems). Moreover, we focus on analysis tools from linear systems theory, such as controllability, reachability observability and stability. We go through the main control strategies for regulation and tracking, e.g., linear state feedback, feedforward control, linear quadratic regulators, model predictive control.
The second part of the course focuses on two main subfields, namely, estimation and model identification, including learning algorithms. We begin with the full/partial state observers (Luenberger observers), and the Kalman filter. We will visit identification algorithms for dynamical systems, such as: linear regression models (ARX, ARMAX), recursive least squares, support vector machines for identification, neural networks and fuzzy systems for identification.
Lectures are complementary to lab exercises and simulation experiments, showcasing methodology, with an emphasis on implementation and mindfulness of computational complexity.
Learning Outcomes
On successful completion of the course the student will be able to:
• Apply state-space methods for modelling and control in real-life applications and in robotics, including software implementation.
•Apply system identification and state estimation methods in real-life applications and in robotics, including software implementation.
• Apply analysis algorithmic tools (for, e.g.,controllability, stability, safety certificates) successfully for dynamical systems.
Skills
• Modelling of complex dynamical systems in engineering.
• Control design and implementation, as well as analysis for dynamical systems.
• Estimator design and implementation,
• System identification.
• Mathematical reasoning.
• Software/Programming skills.
Mechatronics lie at the intersection of electrical, electronic, mechanical engineering and computer science. This hands-on course holistically explores mechatronics, with an emphasis on utilisation in embedded systems and especially robotics. By developing real-time systems using off-the-shelf microcontrollers, we focus on designing systems integrating electronic hardware, microcontrollers, software, sensors, actuators and electric motors. The course is skills-focused, taking place in the labs in the Ashby building. We assume the students have a foundational knowledge in programming languages e.g. Python, C.
The first part of the course introduces microcontrollers (e.g. Arduino), outlining the basic architecture and structure. We focus on basic software and hardware functions using inputs outputs such as introduction to layout, digital inputs and outputs (GPIOs), serial communication, analog to digital converters (ACD), digital to analog converters (DAC), timers, interrupts, PWM signals. Moreover, we introduce the principles of printed circuit boards (PCB) design exploring topics such as PCB design basics and layout, electric components, connectivity, schematics, by using relevant software and hardware e.g. Autodesk fusion for design, TinkerCad for simulation
The second part of the course introduces peripherals e.g. shift registers, segment display modules, keypad matrix, LCD panel. Emphasis is given in interfacing sensors and actuators for microcontrollers. A major group project/coursework will commence during this period, involving the design of a mechatronics system/autonomous systems that will be built to address well defined challenges. We will introduce and focus on functionalities and interconnections of the necessary parts such as lithium-ion batteries, breakout boards, motors, motor drivers, encoders, sensors (ultrasonic, proximity, camera etc), as well as laser-cutting and 3d printing processes.
Learning Outcomes
On successful completion of the course the student will:
- Develop software for an MCU, including event-driven ISR (Interrupt Service Routines).
- Demonstrate knowledge of analogue and digital interface circuits are designed for an MCU, and the hardware structure of the microcontroller.
- Demonstrate knowledge of designing Printed Circuit Boards (PCBs).
- Use analogue and digital interface circuits for microcontrollers, and interface sensors and actuators to microcontrollers.
- Design mechatronic systems for robotic applications
Skills
Use of IDE (Integrated Development Environment) for developing microcontroller software.
- Create, compile, edit and test/debug programs
- Design embedded systems to solve real-world problems
- Develop communication skills for working in a team.
- Develop project management skills for working in a team.
- Devices and technology for microelectromechanical systems (MEMS).
- Design of MEMS sensors
- Sensing methods including piezoresistive and capacitive
- MEMs actuators.
- Fabrication technology for silicon based MEMS
- Reliability issues.
Coursework:
1. Analysis and design of a sensor structure, involving Finite Element Modelling, including attention to its associated fabrication processes.
Labs:
1.Practical exercise using COMSOL as part of coursework 1.
Learning Outcomes
- Knowledge of the world of microelectromechanical devices and systems (MEMS)
- An awareness of material properties, fabrication technologies, basic device structures, sensing and actuation principles.
- Ability to calculate key process and device performance parameters
- Ability to design MEMS devices
Skills
- Ability to design MEMS devices
- Calculation of key process and device performance parameters.
The project involves the application of engineering design techniques to a topic of electrical and electronic engineering which is typically related to the MSc course and modules. In the project specification, the project originator typically endeavours to ensure an element of theoretical research, theoretical analysis, simulation and performance assessment, design development, manufacture, and testing. Note that all projects involve significant hardware and/or software development components.
Learning Outcomes
- All projects require a solid understanding of the associated engineering context, underlying scientific principles and methodology; this is expected at an early stage of the project.
- Most projects require a solid understanding of mathematical methods, including, but not limited to statistics and probability.
- Some projects require a systems approach to solve engineering problems including following a multi/inter-disciplinary approach
- Projects are of research nature involving developing new technologies, or discovering/testing the properties or limitation of existing ones.
- In all projects the students are expected to formulate and analyse the problems of the projects.
- In all projects the students are expected to analyse the simulation data and/or experimental data using first principles of mathematics, statistics, natural science, and engineering principles.
- In many projects, the students are expected to analyse the effects of incomplete and uncertain data.
- Understanding of project context in which engineering knowledge is applied.
- Systems approach in hardware-based projects and projects that take a holistic approach to an engineering problem.
- Use of equipment, processes, products, materials and components in projects.
- All projects involve either a strong hardware element (i.e., the use of lab equipment to analyse complex systems and solve engineering problems) or the development of appropriate software.
- In all projects the students are expected to use technical literature, textbooks, open-source codes, and online resources to solve complex problems.
- In all projects the students are expected to design novel engineering systems, proofs of concept and demonstrate them using prototypes (hardware and/or software)
- In all projects there are some aspects of problem solving and application of technical knowledge.
- In all projects, the students are expected to consider the health and safety, diversity, inclusion, industry standards, etc to design the methodologies of the projects.
- In many projects, the students are expected to consider the environmental impacts and societal impacts of the project’s outcomes (i.e., projects related to environmental pollution or marine debris) and analyse and minimise the adverse impacts of the solutions if they are not designed appropriately.
-Communicate complex ideas in a technically sound and concise manner to a non-expert audience (there is an expectation that the student’s final report is written in such a way that it can be understood by the moderator, who is not familiar with the student’s work).
- Apply an integrated or systems approach to the solve the complex problems in projects.
- Identify and analyse ethical concerns in some projects (i.e., human participation).
- Identifying risk issues and risk mitigation in first report.
-The students are expected to develop practical and laboratory skills and/or software development or programming skills.
-Use of equipment, processes, products, materials and components in projects.
- Proof of concept / prototype projects involve commercial and economic aspects.
- Project management to target specified project objectives.
- Time management throughout project.
- Awareness of health and safety.
- Understanding of risk issues: risk mitigation, health and safety.
- Understanding of innovative aspects.
- Reflection on progress.
-Evaluate the environmental and societal impact of solutions to complex problems and minimise adverse impacts.
Skills
Ability to apply general principles and design or analytical techniques to the solution of engineering problems. This may require theoretical, practical or design skills or a combination of the three.
Normally a 2.2 Honours degree or above or equivalent qualification acceptable to the University in Electrical and/or Electronic Engineering, or Physics with significant electronics content.
AICC/NI Cyber funding: A limited number of fully funded places (provided by the Department for the Economy) are available for this programme for eligible applicants resident in Northern Ireland. Applicants are advised to apply as early as possible in order to be considered for a funded place. You will be notified as soon as possible whether your application has been selected for a funded place. If you have not been selected for a funded place, we will accept self-funded or employer-funded applicants, if spaces are available.
Applicants are advised to apply as early as possible and ideally no later than 30th June 2025 for courses which commence in late September. In the event that any programme receives a high number of applications, the University reserves the right to close the application portal prior to the deadline stated on course finder. Notifications to this effect will appear on the application portal against the programme application page.
Applicants are advised to apply as early as possible and ideally no later than 30th June 2025 for courses which commence in late September.
Please note: a deposit will be required to secure a place.
International Students
Our country/region pages include information on entry requirements, tuition fees, scholarships, student profiles, upcoming events and contacts for your country/region. Use the dropdown list below for specific information for your country/region.
English Language Requirements
Evidence of an IELTS* score of 6.0, with not less than 5.5 in any component, or an equivalent qualification acceptable to the University is required.
*Taken within the last 2 years.
International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.
If you need to improve your English language skills before you enter this degree programme, Queen's University Belfast International Study Centre offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
Academic English: an intensive English language and study skills course for successful university study at degree level
Pre-sessional English: a short intensive academic English course for students starting a degree programme at Queen's University Belfast and who need to improve their English.
Our graduates have found that earning a prestigious MSc qualification from the School, one of the UK's top engineering schools, has significantly enhanced their job opportunities and employment prospects. Graduates typically find employment in a wide range of fields including with semiconductor companies, electronic equipment manufacturers, design and service providers, software houses and in other electronic engineering-based industries.
Queen's postgraduates reap exceptional benefits. Unique initiatives, such as Degree Plus and Researcher Plus bolster our commitment to employability, while innovative leadership and executive programmes alongside sterling integration with business experts helps our students gain key leadership positions both nationally and internationally. http://www.qub.ac.uk/directorates/sgc/careers/
Graduate Plus/Future Ready Award for extra-curricular skills
In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Graduate Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.
1EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.
2 EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.
3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.
All tuition fees quoted relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.
There are no specific additional course costs associated with this programme.
All Students
Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.
Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.
Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.
If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.
Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.
There are also additional charges for graduation ceremonies, examination resits and library fines.
How do I fund my study?
The Department for the Economy will provide a tuition fee loan of up to £6,500 per NI / EU student for postgraduate study. Tuition fee loan information.
A postgraduate loans system in the UK offers government-backed student loans of up to £11,836 for taught and research Masters courses in all subject areas (excluding Initial Teacher Education/PGCE, where undergraduate student finance is available). Criteria, eligibility, repayment and application information are available on the UK government website.
