Queen’s University Belfast, United Kingdom invites online Application for number of Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at Queen’s University Belfast, United Kingdom.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: Jetset Your Degree – Financial Awards for Skill Development – 2024 – 2025
The AHSS JETSET award will support you in being able to take part in an international activity during the 24/25 academic year that will enrich your degree and support your skill development relevant to your chosen career.
This award is available to current QUB students studying within the Faculty of Arts, Humanities and Social Sciences.
Deadline : 1 February 2025.
(02) PhD Degree – Fully Funded
PhD position summary/title: Vegetation-wave interactions on mixing: transverse mixing of soluble pollutants around seagrass beds
We are looking for a motivated candidate to lead a research project on pollution transport around coastal vegetation. The research project comprises an experimental study where pollution transport around vegetation is modelled in a large-scale experimental facility. A collection of measurement devices will be used for this study. The study aims to correlate water quality and quantity effects, identifying the contributions from different hydrodynamic conditions on mixing characteristics. This project will enhance the current understanding of pollution transport mechanisms, enabling effective wastewater treatment practices, disaster management strategies, and environmental conservation plans.
Deadline : 30 June 2025
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(03) PhD Degree – Fully Funded
PhD position summary/title: Place and the Dynamics of Social Deprivation in Belfast 1991-2021
The project will examine why some parts of Belfast have remained deprived since the 1990s when others have either become more or less deprived. It will tie into academic debates about the dynamics of social deprivation; for example, if areas change their deprivation profiles because of differential population inflows and outflows or because residents who remain in place become on average more (or less) deprived. Of particular interest is path dependency within neighbourhoods, the intergenerational transmission of social deprivation between parents and children, and how far neighbourhood is important in shaping life chances. It is envisaged that the research will use the Northern Ireland Longitudinal Study (NILS) for secondary data analysis (https://nils.ac.uk/) to profile places and populations between 1991 and 2021 to explore persistent individual and neighbourhood social deprivation and the place, family, and individual factors associated with transitions into and out of deprivation but that there will be primary data collection via interviews, focus groups, or small-scale surveys as necessary. This project has great potential for policy and societal impact and opportunity to work with external partners. A 3-month placement within the Department for Economy will also be included, providing the student with valuable experience of working in a policy environment and networking opportunities.
Deadline : 30 April 2025
(04) PhD Degree – Fully Funded
PhD position summary/title: Migration, education, and development in the West of Northern Ireland
The research will try to answer the above question by considering population flows into and out of the West of NI between 1991 and 2021, analysing the demographic backgrounds of people who move to and from the area. Particular topics of interest include young people at entry and exit from higher education, return migration over the life cycle, the occupational and educational composition of in- and out-flows, and migration and its relationship to long-distance commuting. The project will make use of the new linkage of the 2021 Census data to the Northern Ireland Longitudinal Study (NILS) to analyse migration (and staying) patterns and trends from 1991 as a context for the research but primary data collection via methods such as interviews, focus groups, and small-scale surveys will also be needed as appropriate. This project has great potential for policy and societal impact and opportunity to work with external partners. A 3-month placement within the Department for Economy will also be included, providing the student with valuable experience of working in a policy environment and networking opportunities.
Deadline : 30 April 2025
(05) PhD Degree – Fully Funded
PhD position summary/title: Distribution, range and ecological impacts of non-native small mammals
This 3-year PhD will assess the distribution, range, abundance and ecological impacts of non-native small mammals in Northern Ireland focusing on the bank vole, field vole and potentially greater white-toothed shrew.
Ireland has a biogeographically unique community of flora and fauna shaped by its geological and human history. Like most islands it is also highly vulnerable to invasion by non-native species, frequently brought deliberately or accidently by people, which have disproportionate negative impacts on native species. Ireland has been invaded by four non-native small mammals: 1) the bank vole, 2) greater white-toothed shrew, 3) hazel dormouse and 4) field vole. These impact native fauna causing wood mouse population declines, pygmy shrew extirpation, changes in bird of prey productivity and a dramatic collapse of invertebrate biomass with probable trophic cascades likely affecting the wider ecosystem. The bank vole was first recorded in Northern Ireland at Crumlin near the Lough Neagh ASSI (Area of Special Scientific Interest) in 2020 and the field vole in Slievenacloy ASSI in 2023, and Slieveanorra and Croaghan ASSI in 2021; all designated for their biodiversity. The vector for these introductions is currently unknown. There is also reason to speculate that the greater white-toothed shrew may be present in Counties Fermanagh and Antrim, though this remains to be confirmed.
Deadline : 31 March 2025
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(06) PhD Degree – Fully Funded
PhD position summary/title: Phylogenomic history, systematic status and conservation assessment of the endemic Irish hare
The proposed PhD will focus on the Irish hare (Lepus timidus hibernicus), one of Ireland’s most significant endemic species, with the aim of clarifying its taxonomic status, understand its population structure, and assess its current conservation status. The study will investigate whether the Irish hare should be considered a distinct species from the mountain hare (L. timidus). A determination in favour of species distinction could have substantial implications for conservation.
Deadline : 31 March 2025
(07) PhD Degree – Fully Funded
PhD position summary/title: Safeguarding drinking water from problem aquatic species: development and application of emerging technologies
Problematic aquatic species are causing substantial burdens to the production of drinking water supplies. These problems are escalating with climate change, habitat degradation, and biological invasions, requiring proactive management with sustainable approaches. This fully-funded, transdisciplinary PhD brings together academia and industry to develop and implement practical solutions to mitigate impacts of harmful aquatic species, such as cyanobacteria and fouling bivalves, on drinking water treatment. Thereby, it will address global Sustainable Development Goals, including #3, #6, and #14.
Deadline : 31 March 2025
(08) PhD Degree – Fully Funded
PhD position summary/title: AI-driven Radio Frequency Semiconductor On-wafer Measurement System for Future Industrial Applications
This exciting PhD project aims to revolutionize the characterization of RF semiconductor devices by integrating cutting-edge AI-driven measurement techniques. With the ever-growing demand for precise and reliable on-wafer measurement systems, this research focuses on advancing the tools essential for ensuring the quality and performance of RF electronic products during their development and quality assurance stages.
Deadline : 28 March 2025
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(09) PhD Degree – Fully Funded
PhD position summary/title: Novel Feature-Based Thermal Topology Optimisation
Structural design and optimisation are crucial for a sustainable future. Incorporating new technologies, increased electrification and the use of new materials all mean increased thermal consideration is essential for new designs. Topology optimisation enables the design of lightweight and highly efficient structures by optimising the distribution of material within a given design space and subject to specific loads and constraints. It allows for the creation of innovative lightweight structures that would be difficult or impossible to design manually. While topology optimisation offers significant benefits, its wider adoption is hindered as not all approaches include thermal considerations when coming up with new designs, and the resulting designs are often not easily incorporated back into a CAD system.
Deadline : 14 March 2025
(10) PhD Degree – Fully Funded
PhD position summary/title: Topology optimisation for innovative geometric solutions with prescribed manufacturing technologies
Structural design and optimisation are crucial for a sustainable future. Minimizing material and energy use during production and operation reduces environmental impact and conserves resources for future generations. Topology optimisation enables the design of lightweight and highly efficient structures by optimising the distribution of material within a given design space and subject to specific loads and constraints. It allows for the creation of innovative lightweight structures that would be difficult or impossible to design manually. While topology optimisation offers significant benefits, its wider adoption is hindered as designs often involve intricate geometries which cannot be cost effectively manufactured.
Deadline : 14 March 2025
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(11) PhD Degree – Fully Funded
PhD position summary/title: Topology optimisation for innovative geometric solutions with prescribed manufacturing technologies
The project will build on established techniques developed at Queen’s using the Moving Morphable Components (MMC) method for topology optimisation. The MMC technique overlays geometric components (geometric entities such as lines or curves) on an analysis mesh. Components have their own parameterisation schemes (choice of values which when varied change the shape) providing flexibility. Finite Element calculations using the mesh enable calculation of the performance of the design, alongside the calculation of sensitivities which inform how the components should be evolved. The project will build on the established techniques, and will develop and extend the techniques considering exemplar design challenges provided by Rolls-Royce.
Deadline : 14 March 2025
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(12) PhD Degree – Fully Funded
PhD position summary/title: Novel Feature-Based Thermal Topology Optimisation
This PhD will improve upon the work by Shannon et al. and apply this to the topology optimisation of thermal problems. Given the reduction in the number of design variables, it becomes possible to leverage gradient-enhanced surrogate modelling techniques within the optimisation. This not only guarantees a global optimum but also provides a mechanism by which multiple objectives and constraints can be included. The existing surrogate modelling toolset developed at the University of Southampton also enables multiple levels of simulation fidelity and even categorical variables to be employed within such an optimisation[2]. Southampton’s existing work on neural implicit geometry approaches[3,4], offers a way in which existing libraries of geometric features could be combined within a single parametric feature which can be moved and scaled within the topology optimisation. This offers a way in which existing engineering best practice and novel geometric concepts can potentially be fused and explored together as part of a single topology optimisation.
Deadline : 14 March 2025
(13) PhD Degree – Fully Funded
PhD position summary/title: Retrodirective antenna technology for fast tracking of supersonic platforms
Ultrafast communications between mobile platforms is essential for a wide range of applications, with the users expectations for high speed, reliability and low latency are ever increasing. Establishing fast reliable comms links becomes even more challenging when required between platforms moving at high relative velocities. This PhD will look at how “retrodirective” antennas can be used to provide an ultrafast tracking and resilient comms link between airborne platforms, or from ground platforms to air platforms.
Deadline : 28 February 2025
(14) PhD Degree – Fully Funded
PhD position summary/title: Finding “home” in a cultural landscape of migration and belonging
Detailed description of the project. Migration plays a vital role in Northern Ireland’s history, its present, and its future. This became particularly prominent over the summer of 2024 when anti-immigrant rhetoric made the news and filled the streets across the UK and Ireland. Crucial within these conversations are Ireland’s own histories of migration. How these histories are constructed and feed into contemporary, popular narratives of home and belonging remains contentious, especially in Northern Ireland. At the heart of this project is the question: what shapes the ‘story’ of history?
Deadline : 10 March 2025
(15) PhD Degree – Fully Funded
PhD position summary/title: Deep Learning for Enhanced Neural Decoding in Real-Time Brain-Computer Interfaces (BCIs)
The successful candidate will develop and evaluate novel deep learning models that can accurately decode neural activity from EEG data associated with specific tasks (e.g., imagining hand or leg movements). The project will explore various deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to capture the spatial and temporal patterns of brain activity. The candidate will also investigate real-time optimization techniques to enable responsive, low-latency BCI control suitable for real-world applications.
Deadline : 28 February 2025
(16) PhD Degree – Fully Funded
PhD position summary/title: Predicting User Performance for Enhanced Brain-Computer Interfaces
The project aims to develop a robust predictive model that can anticipate BCI user performance based on a variety of factors. This project’s main objectives are:
1. Develop Predictive Models for BCI User Performance: Leveraging machine learning and statistical techniques, this project will create predictive models capable of estimating BCI user performance. A wide array of algorithms, including support vector machines, deep learning models, and regression analysis, will be evaluated for their predictive accuracy. These models will be rigorously trained and tested using collected data and publicly available datasets.
2. Explore User-Specific Factors Impacting BCI Control: The project aims to integrate predictive models into BCI systems, enabling real-time adaptation of system parameters based on user performance. The primary objective is to enhance BCI user control and overall experience.
3. Establish a User-Centric Framework for BCI System Adaptation: This research project will offer valuable insights into creating a user-centric framework for BCI system adaptation, enriching the personalized user experience, and broadening the accessibility of BCI technology.
Deadline : 28 February 2025
(17) PhD Degree – Fully Funded
PhD position summary/title: Hybrid Energy Storage Systems for Wind Energy in Medium Voltage DC Applications
This project aims at exploring a hybrid energy storage system (HESS) that combines multiple energy storage technologies; batteries and supercapacitors to optimise the utilisation of wind power in MVDC networks. The project will focus on designing adaptive control that facilitate the integration of HESS to respond to the dynamic changes of wind generation and variable loads. An advanced model predictive control will also be designed to optimise the operation of the HESS while maintaining high power quality. This includes addressing frequency stability and rapid response to grid disturbances.
Deadline : 28 February 2025
(18) PhD Degree – Fully Funded
PhD position summary/title: Virtual-Reality Enhanced BCI Neurofeedback for Stress Management
The primary objective of this PhD project is to design and validate a VR-enhanced BCI neurofeedback system that leverages electroencephalography (EEG) signals to monitor stress levels and provide users with immediate, real-time feedback in an immersive VR environment. The BCI will track and classify brainwave patterns associated with stress, focusing on markers like alpha, beta, and gamma rhythms. Users will receive feedback on their brain activity through visual and auditory cues in a VR setting, allowing them to engage in self-regulation exercises, which are expected to lead to lower stress levels over time.
Deadline : 28 February 2025
(19) PhD Degree – Fully Funded
PhD position summary/title: Adaptive Modular Converter-Based Energy Storage for Grid-Optimal Virtual Synchronous Generators
The modular power converter-based energy storage system has emerged as a cutting-edge technology in renewable energy integration and grid stability. Nonetheless, adaptive and optimal control for these systems in virtual synchronous generators still needs to be explored in the existing literature. This research will explore an adaptive and optimal virtual synchronous generator technology for modular-based energy storage systems. An advanced model reference adaptive controller will be designed and advanced intelligent control technology will be employed to optimise the parameters of the reference model in the adaptive controller, effectively adapting to variations in power grid dispatching. The project is expected to develop a novel adaptive observer to compensate for negative sequence voltage under unbalanced grid voltage. A comparative study will be conducted to compare the proposed methods with traditional approaches such as PQ control, droop control, and primary virtual synchronous generator control.
Deadline : 28 February 2025
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(20) PhD Degree – Fully Funded
PhD position summary/title: Addressing Challenges of Multi-Modal Data Analysis in Application to Precision Medicine
Precision medicine aims to tailor healthcare interventions based on individual differences in patients’ genetic, environmental, and lifestyle factors. This personalized approach has gained momentum through the availability of diverse biomedical data modalities, which provide comprehensive insights into human health and disease. Examples of biomedical data modalities include: genomic data (high-dimensional sequences representing genetic information), imaging data (rich visual information from modalities like MRI or CT scans, often with a large spatial resolution but comparatively low feature dimensionality), clinical records (longitudinal data capturing patients’ medical histories, treatment plans, and outcomes, generally unstructured and variable in length). Multi-modal data analysis, which combines various types of data, is critical for advancing precision medicine as it can reveal complex patterns and relationships across datasets that, when analysed individually, might fail to yield actionable insights. For instance, fusing imaging data with genomic information can enhance cancer diagnosis by linking tumour phenotypes to specific genetic mutations, while combining patient records with proteomic profiles can facilitate more precise predictions of treatment responses. Given these diverse data sources, combining them effectively in a machine learning (ML) model is crucial yet fraught with several major difficulties.
Deadline : 28 February 2025
(21) PhD Degree – Fully Funded
PhD position summary/title: Hardware assisted homomorphic encryption (HE) acceleration.
This project will explore algorithmic and architectural optimizations by designing and developing a specially optimized hardware accelerator for these somewhat homomorphic encryption schemes on FPGA. In the HE setting, there are two entities, client and cloud. The client encodes and encrypts its data and sends it to the cloud where homomorphic computations are performed on the encrypted data while keeping it confidential. As the processed data is sent back to client, it reads the processed data from the cloud in the encrypted format, and then decrypts and decodes it. While there is more work done today on accelerating cloud-side operations on high-end FPGA and ASIC platforms, very few earlier works focuses on the client side which is critical and challenging since the client side may use low end devices with stringent resource and performance constraints. The CKKS scheme (2) has emerged as a promising HE scheme as it allows computations on real numbers and consequently can cater wider range of applications. This research will explore various microarchitecture for the computationally intensive components in HE for acceleration via pipelining, parallelism, and memory access patterns. A benchmarking in terms of performance, power consumption, and cost will help understand the trade-offs in the design. OpenFHE will serve as a software baseline (3). Security enhancement of the hardware design against the side channel attacks will also be identified and addressed.
Deadline : 28 February 2025
(22) PhD Degree – Fully Funded
PhD position summary/title: Advanced AI Methods for Analysing Nutritional Influences on Cognitive and Mental Health
Cognitive and mental health conditions, including anxiety, depression, and neurodegenerative diseases, affect millions of people worldwide, posing a significant public health challenge. Emerging research underscores that nutrition plays a critical role in influencing mental wellbeing and cognitive functions. Nutritional intake—alongside factors like medication, lifestyle, and environmental influences—affects brain function, neurotransmitter balance, and overall mental well-being. However, identifying the specific dietary patterns that contribute positively or negatively to mental health is complex, as these influences interact in subtle ways over time and vary based on individual factors. A clearer understanding of how nutrition impacts mental and cognitive health could lead to innovative approaches to mental health care, with potential to complement medication and psychotherapy. Detecting dietary patterns associated with positive mental health outcomes could help in developing personalized nutrition plans that support mental well-being, potentially reducing the risk of conditions such as depression, anxiety, and cognitive decline. Advanced AI methods, particularly machine learning (ML) and deep learning (DL) techniques, provide a promising avenue for detecting complex, non-linear relationships in extensive datasets, making them ideal for analysing data on nutritional, medication, and cognitive health. Despite this promise, studying the links between nutrition and cognitive health presents unique challenges, including the need to accurately quantify dietary intake, handle high-dimensional, heterogeneous data, and capture time-dependent effects that influence mental health outcomes. This project will develop a robust, data-driven foundation for understanding the role of nutrition and its interaction with medication in cognitive and mental health, with a focus on complex and hidden patterns that may offer new insights into personalized health interventions (e.g. dietary interventions for reducing symptoms of anxiety and depression).
Deadline : 28 February 2025
(23) PhD Degree – Fully Funded
PhD position summary/title: Fault tolerant post quantum cryptography systems for satellite communications
The number of space-based entities and missions is experiencing explosive growth, supporting critical applications like global communication, disaster relief, and national security. These missions rely on secure communication for data exchange and command and control. The longevity of satellites and their associated infrastructure along with the difficulty of changing anything after the launch requires long-term public key cryptography security solutions. The projected arrival of powerful quantum computers within the next decade poses a threat to the current cryptographic systems used for satellite communication. Existing public-key cryptography standards will become vulnerable, potentially compromising critical infrastructure and sensitive data. This project will provide the first comprehensive analysis and implementation of novel Quantum-resistant cryptographic communication implementation in the context of the corresponding requirements for the use-case of aerospace and satellite communication. We will follow the recommendations from the NIST initiated the Post Quantum Cryptography (PQC) competition that announced the first suite of quantum-safe cryptographic algorithm schemes after three rounds in 2022 to replace the Public Key Cryptography standards in use today. Space environments are challenging due to a variety of reasons, e.g., their strict requirements (limited bandwidth of the telecommand uplink channel, high communication latency, low power). In addition, due to the harsh physical environment in space the security protocols, the security solutions should be fault tolerant both at the algorithmic level and the physical level (e.g., the use of specialised Space-Grade PFGAs that exhibit lower performance in comparison).
Deadline : 28 February 2025
(24) PhD Degree – Fully Funded
PhD position summary/title: Zero Trust Power Management for On-Chip Systems
The student will develop a secure power management system for on-chip systems that integrates a novel power distribution controller with zero trust architecture principles to enhance security, efficiency, and resilience. The project will involve implementing algorithms to dynamically allocate power based on real-time demand and security requirements. Use machine learning to adapt power distribution according to the behaviour and needs of different components. Ensure the integrity of the system from power-on by verifying the authenticity and integrity of firmware and software. Create isolated segments within the chip to contain potential breaches and limit the spread of attacks. Apply security policies tailored to each segment to enhance protection. Continuously verify the identity and trustworthiness of components and data flows within the system. Use control algorithms to monitor and analyse system behaviour for anomalies that may indicate a security threat. Adjust access permissions in real-time based on contextual information such as power usage patterns, device health, and user behaviour. Enforce strict access controls using zero trust principles to ensure only authorized components can access critical resources. Continuously monitor power usage and system performance to detect anomalies. Implement automated response mechanisms to isolate affected segments and mitigate security threats detected through power usage anomalies.
Deadline : 28 February 2025
(25) PhD Degree – Fully Funded
PhD position summary/title: Enhancing Arithmetic Efficiency in Lattice-Based Post-Quantum Cryptography Hardware Accelerators
The aim of this project is to enable high arithmetic efficiency in Lattice-Based Post-Quantum Cryptography (PQC) by developing new implementations that address the overheads of conventional PQC hardware accelerators. This project will extract and profile the inefficient arithmetic structures used in implementing PQC and redesign them with compact number representations. This enables high utilization of accelerator datapaths without losing precision. Throughout this project, a variety of techniques, including approximate computing and customized residue number systems, will be utilized to achieve high-performance and practical hardware implementations of PQC.
Deadline : 28 February 2025
(26) PhD Degree – Fully Funded
PhD position summary/title: Decentralized Trust Zone for Remote Health Monitoring
The student will establish a decentralized network of AI models deployed on edge devices to process health data locally. Implement federated learning techniques to enable AI models to learn from data across multiple devices without sharing raw data. Develop secure communication protocols for model updates and aggregation. Integrate basic health monitoring sensors and devices to collect initial data for model training. Enhance the AI models with predictive analytics capabilities and integrate blockchain for secure data management. Develop algorithms for early detection of health issues based on decentralized data. Implement blockchain technology to manage data access permissions and ensure data immutability. This project will result in a decentralized AI system capable of providing real-time, personalized health insights while maintaining the highest standards of data privacy and security. It aims to revolutionize remote health monitoring by making it more proactive, personalized, and resilient.
Deadline : 28 February 2025
(27) PhD Degree – Fully Funded
PhD position summary/title: Software-Defined FHE: Rethinking Homomorphic Encryption Libraries via Software-Defined Arithmetic Optimizations
The goal of this project is to explore software-defined computer arithmetic and develop new numerical formats with their corresponding customized arithmetic operations tailored for homomorphic encryption. A highly efficient software library will be created to facilitate the widespread use of FHE, making it suitable for processing encrypted data in machine learning environments.
Deadline : 28 February 2025
(28) PhD Degree – Fully Funded
PhD position summary/title: Side-channel analysis countermeasure of Quantum Safe implementations on hardware (aims to FPGA)
The conventional cryptography is claimed will be broken due to the appearance of Quantum Computer. Various PostQuantum crypto (PQC) or Quantum Safe algorithms are proposed and went to standardization like Lattice-based CRYSTALS-Kyber. However, the implementation of those algorithms in software leaks sensitive information (like private key) to side-channel (like electromagnetic field, power consumption and execution time) and can be revealed in less than 50 traces (50 decryption times) with the help of machine learning or using conventional correlation power analysis (CPA). This project aims to identifying actual and potential leakage operation of the implementations on hardware, aiming to FPGA implementation before applying various hardware oriented SCA countermeasure methods like dual-rail logic, dual-rail memory, various hardware dedicated multiplication, masking (additive, multiplicative and affine), shuffling to make the implementation of PQC safer under SCA.
Deadline : 28 February 2025
(29) PhD Degree – Fully Funded
PhD position summary/title: Side-channel analysis countermeasure of Quantum Safe implementations on microcontroller
The conventional cryptography is claimed will be broken due to the appearance of Quantum Computer. Various PostQuantum crypto (PQC) or Quantum Safe algorithms are proposed and went to standardization like Lattice-based CRYSTALS-Kyber. However, the implementation of those algorithms in software leaks sensitive information (like private key) to side-channel (like electromagnetic field, power consumption and execution time) and can be revealed in less than 50 traces (50 decryption times) with the help of machine learning or using conventional correlation power analysis (CPA). This project aims to identifying actual and potential leakage operation of the implementation on software before applying various SCA countermeasure methods like masking (additive, multiplicative and affine), shuffling to make the implementation safer under SCA.
Deadline : 28 February 2025
(30) PhD Degree – Fully Funded
PhD position summary/title: Effectiveness of micro shuffling to Side-channel analysis in Quantum Safe implementations on microcontroller for light-weighted crypto implementation
SCA is a real threaten to all cryptographic devices. Even though various SCA countermeasure methods like masking and functions shuffling are applied, crypto implementations are still leak sensitive information to side-channel and be able to attack. In addition, those countermeasure methods add additional executions and require more computational resources like memory, execution time and power. This project tries to increase the security of crypto implementation under SCA by applying micro shuffling in instruction and microcontroller architecture level while avoiding complex countermeasure methods like masking.
Deadline : 28 February 2025
(31) PhD Degree – Fully Funded
PhD position summary/title: Explainable machine learning (ML) models for side-channel analysis (SCA)
The crypto algorithm is safe in theory, but their implementations still leak information to side-channel and can be attacked with Differential Power Analysis (DPA), Correlation Power Analysis (CPA) and state-of-the-art machine learning (ML). Even though SCA countermeasure method like masking is applied to make the implementations safer under DPA and CPA, ML can learn the features of the mask and the sensitive data and be able to attack the protected implementations. The training and attacking process of ML does not show what and where are those features come from, and so cannot explain how a ML model can successfully combine those leakage in analysis. It leads to the requirement of explainable ML models to identify leakage locations that are combined in the attack so that additional countermeasure methods can be applied to the correct executions to strengthen the security of crypto under SCA.
Deadline : 28 February 2025
(32) PhD Degree – Fully Funded
PhD position summary/title: Hardware Security for Approximate Computing
Silicon physical unclonable functions (PUFs), which exploit manufacturing variations of silicon chips, offer a promising mechanism that can be used in many security, protection and digital rights management applications. Such a primitive has a number of desirable properties from a security perspective, such as the ability to provide a low-cost unique identifier for an integrated circuit (IC) or to provide a variability aware circuit that returns a device specific response to an input challenge. This gives it an advantage over current state-of-art alternatives such as secure non-volatile memory (NVM) or trusted platform modules (TPMs). No special manufacturing processes are required to integrate a PUF into a design. This lowers the overall cost of the security for the IC enabling the PUF to be utilised as a hardware root of trust for all security or identity related operations on the device. An approximate dynamic random-access memory (DRAM)-based PUF design was proposed. It is the only PUF design based on approximate memory to enhance the security of approximate computing. Unfortunately, it has been found vulnerabilities. Currently, no comprehensive research has been conducted into the security of approximate computing or into countermeasures that protect such designs. The use of approximation strategies can even provide an advantage to attackers, since common security components in a standard computing system will likely be excluded from approximated components, as existing security algorithms cannot work in an approximated way. Intrinsic security strategies, such as PUFs that use circuitry already present in the system, without modification, and purely by means of software, are promising to address the challenges in approximate computing. Ultimately, how to achieve secure and lightweight security designs for approximate computing is a critical challenge that this proposal seeks to address.
Deadline : 28 February 2025
(33) PhD Degree – Fully Funded
PhD position summary/title: Sub-Terahertz Antennas for 3D Heterogeneous Integration
This project proposes to investigate the design and integration of sub-terahertz antennas and arrays operating in the G-band, with a focus on creating compact, high-efficiency phased arrays and massive MIMO systems. The study will incorporate silicon-based RF integrated circuits (ICs) and III-V compound semiconductor MMICs, aiming to achieve compact, high-performance, cost-effective sub-terahertz antennas with 3D heterogeneous integration for future data-intensive applications.
The proposed project seeks to develop sub-terahertz antenna architectures and integration techniques optimized for sub-terahertz communications, specifically targeting the design of antennas that integrate seamlessly with silicon and III-V semiconductor technologies.
Deadline : 28 February 2025
(34) PhD Degree – Fully Funded
PhD position summary/title: Dual-Functional Millimeter-Wave Antennas for Integrated Sensing and Communication in 6G Networks
The proposed research will develop unique mmWave antenna and array designs that allow simultaneous communication and sensing functions, particularly suited to the demands of 6G networks. Addressing the typical drawbacks at mmWave frequencies, the design will utilize shared aperture antennas for efficient resource allocation between communication and MIMO radar sensing, thus enhancing indoor coverage and reliability without excessive hardware requirements. By focusing on novel antenna structures capable of high-resolution beam forming and holographic beam focusing, the project aims to enable precise user equipment (UE) positioning, adaptable to real-time movement in indoor settings.
Deadline : 28 February 2025
(35) PhD Degree – Fully Funded
PhD position summary/title: Anti-counterfeiting Techniques Design and Analysis for IoTs
According to Cisco, 500 billion devices are expected to be connected to the Internet by 2030. The Covid-19 pandemic, resulting in remote working and home-schooling, is leading to a multiplier effect on rising computing technologies. As devices are connected to the Internet, this opens up a range of new attack vectors for malicious adversaries and hackers. There has been a significant increase in attacks and threats directed at networks in 2020, including the infamous the internet of things (IoT) botnet attacks, which can harvest confidential data and execute cyber-attacks by taking control of the victim’s devices and systems remotely. Additionally, counterfeit devices are an increasing problem as more and more devices are connected online. To address this, this project explores the potential of emerging digital technologies, such as hardware security, machine learning and IoT, to transform the way we design, manufacture, and operate products and services. The project offers a bespoke research and training programme that aims to develop students into cross-disciplinary thinkers and leaders who will influence the roadmaps of future advanced technologies and their applications. They will have a balanced understanding of ICT (security and data analytics) in the context of their application to advanced technologies and high value designs.
Deadline : 28 February 2025
(36) PhD Degree – Fully Funded
PhD position summary/title: Sub-Millimeter Wave Antennas for High-Resolution Terahertz Imaging Systems
This project aims to develop a high-performance antenna system tailored specifically for imaging applications in the sub-millimeter wave region. Operating at 300 and 500 GHz, the proposed antenna systems will address key imaging requirements, including fine spatial resolution, wide field-of-view (FOV), and adaptability to varying imaging distances, making it suitable for applications such as security screening and industrial inspection.
Deadline : 28 February 2025
(37) PhD Degree – Fully Funded
PhD position summary/title: Secure computation with advanced cryptography
As data generation and usage increases across our daily lives, there is a need and also a regulatory requirement to consider user privacy. There are many ways to address this challenge; one technological solution is to use advanced cryptography to enable privacy-preserving computation. One technique is Homomorphic Encryption (HE), which is an exciting, advanced type of encryption, which allows computations on encrypted data, without use of a decryption key. HE can be used for secure computation in a variety of privacy-prioritising applications, finance to healthcare and beyond. Introduced in 2009, such a powerful encryption tool enables privacy-preserving data analysis, however HE suffers in terms of performance due to high computational complexity and particularly memory management demands. Additional it is not well understood, though there is ongoing efforts in industry and standardisation to address this challenge. Hardware acceleration and optimisation of homomorphic encryption has demonstrated successful speed up factors of over 100x for homomorphic encryption. Moreover, such HE schemes are often based on lattice based cryptography, relying on security via added noisy error vectors, which allow for potential approximation and acceleration. Further research is needed to investigate the hardware acceleration of homomorphic encryption, balancing performance, security, approximation, and accuracy, to facilitate high performance implementations at the edge.
Deadline : 28 February 2025
(38) PhD Degree – Fully Funded
PhD position summary/title: Advancing Future Post-Quantum Digital Signatures
Digital signatures are used widely to ensure authentication, this means Alice can check that she received a message from Bob and not Eve. Signatures are used in important cryptographic protocols such as TLS to enable secure communications over a network. Moreover, there are additional types of signature schemes, such as group signatures or threshold signatures, which offer functionality for multiple parties for a variety of interesting applications. Given the ongoing advancements in quantum computing, there is an effort to ensure our current public key cryptography withstands attacks via known classical and quantum algorithms, such as Shor’s algorithm, and to ensure diversity and security of cryptography. With this, there are ongoing global standardisation efforts in digital signatures, with new schemes introduced and requiring further analysis in terms of performance and security. This project aims to advance the state of the art in post-quantum cryptographic digital signatures.
Deadline : 28 February 2025
About Queen’s University Belfast, United Kingdom: Official website
Queen’s University Belfast (informally Queen’s or QUB) is a public research university in Belfast, Northern Ireland, United Kingdom. The university received its charter in 1845 as “Queen’s College, Belfast” and opened four years later.
Queen’s offers academic degrees at various levels, with approximately 300 degree programmes available. The current president and vice-chancellor is Ian Greer. The annual income of the institution for 2019–20 was £400 million of which £88.7 million was from research grants and contracts, with an expenditure of £372.7 million.
Queen’s is a member of the Russell Group of research intensive universities, the Association of Commonwealth Universities, the European University Association, Universities UK and Universities Ireland. The university is associated with two Nobel laureates and one Turing Award laureate.
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