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
Deadline : 31 August, 2025
(02) PhD Degree – Fully Funded
PhD position summary/title: Deep Learning for Enhanced Neural Decoding in Real-Time Brain-Computer Interfaces (BCIs)
Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices, holding transformative potential for fields such as neurorehabilitation, assistive technology, and human-computer interaction. This project specifically aims to harness advancements in deep learning to increase the accuracy, speed, and reliability of BCI systems.
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
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(03) 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
(04) PhD Degree – Fully Funded
PhD position summary/title: Virtual-Reality Enhanced BCI Neurofeedback for Stress Management
Stress is increasingly recognized as a major public health concern with high prevalence across diverse demographics. Frequent stress has been linked to adverse health outcomes, including anxiety, depression, and cardiovascular issues. As the demand for accessible and effective stress management approaches grows, traditional methods – such as cognitive-behavioural therapy (CBT), mindfulness, and meditation – present limitations; they are often time-intensive, costly, and require practitioner involvement. This project aims to explore Brain-Computer Interface (BCI) neurofeedback combined with virtual reality (VR) as a potentially more accessible, interactive, and immersive approach to managing stress.
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
(05) PhD Degree – Fully Funded
PhD position summary/title: Hybrid Energy Storage Systems for Wind Energy in Medium Voltage DC Applications
Connecting wind power to existing power systems offers opportunities and challenges as the world shifts to more renewable energy resources. Medium Voltage DC (MVDC) networks are emerging as a practical solution for efficiently transmitting and distributing power. However, the intermittency of wind energy can lead to fluctuations in power quality and system reliability. Hybrid Energy Storage Systems (HESS) can address these problems as they incorporate multiple storage devices such as batteries and supercapacitors to enhance the performance and responsiveness of the network stability. HESS can provide essential services such as energy smoothing, frequency regulation and peak shaving which makes them flexible for MVDC applications. Integrating HESS with wind can create a stable and reliable energy supply while maximising energy exploitation from renewable energy resources. HESS also permits for tailored designs that adapt to the demands of variable wind conditions and load profiles.
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
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(06) PhD Degree – Fully Funded
PhD position summary/title: Adaptive Modular Converter-Based Energy Storage for Grid-Optimal Virtual Synchronous Generators
With the large-scale integration of renewable energy sources like wind and solar into power grids, the decline in traditional synchronous machines has reduced system inertia, compromising grid stability. Energy storage technology is now widely used to address these challenges. Among these, modular multilevel converter-based energy storage systems have emerged as a promising solution due to their direct grid connection, excellent transient and steady-state performance and flexible applications. Moreover, applying virtual synchronous generators to these systems allows them to mimic the behaviour of traditional synchronous generators, improving frequency and voltage stability. This makes investigating virtual synchronous generators for modular converter-based energy storage systems highly relevant and practically important.
Deadline : 28 February 2025
(07) 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
(08) 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
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(09) 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
(10) PhD Degree – Fully Funded
PhD position summary/title: Fault tolerant post quantum cryptography systems for satellite communications
This project will take up the NIST standardised PQC key establishment and digital signature schemes and will design, test and optimise their EDAC (error detection and correction) circuitry to detect natural faults caused by device malfunctions and the and space radiation caused single event upsets (SEUs). EDAC is crucial to enable reliable functionalities of sensitive and secure satellite systems with stringent constraints. Lattice based cryptography has emerged as one of the most viable classes of PQC algorithms in the NIST PQC competition, however, several aspects relating to the practicality of this schemes for space communications protocols and its fault tolerance has not been thoroughly evaluated.
Deadline : 28 February 2025
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(11) PhD Degree – Fully Funded
PhD position summary/title: Zero Trust Power Management for On-Chip Systems
The project aims to enhance the security and efficiency of on-chip systems by integrating advanced power management techniques with zero trust architecture principles. This project focuses on developing a novel power distribution controller that dynamically allocates power based on real-time demand and security requirements, while continuously verifying the identity and trustworthiness of system components. By combining secure boot mechanisms, micro-segmentation, and anomaly detection, this project seeks to create a robust and resilient on-chip system capable of defending against cyber threats.
Deadline : 28 February 2025
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(12) PhD Degree – Fully Funded
PhD position summary/title: Decentralized Trust Zone for Remote Health Monitoring
This project aims to develop a decentralized trust zone using AI for predictive health analytics, focusing on enhancing remote health monitoring while ensuring data privacy and security. By leveraging federated learning and blockchain technology, the project seeks to create a robust, scalable, and privacy-preserving solution for continuous health monitoring and early detection of potential health issues.
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
(13) PhD Degree – Fully Funded
PhD position summary/title: Software-Defined FHE: Rethinking Homomorphic Encryption Libraries via Software-Defined Arithmetic Optimizations
Number systems underpin computing broadly by encoding how numbers are represented and how key arithmetic operations are performed, thereby determining the efficiency and applicability of fundamental arithmetic operations. Recent advances in machine learning have highlighted the benefits of optimizing number systems for specific applications. This PhD project aims to deliver similar benefits for computations on encrypted data. Fully Homomorphic Encryption (FHE) enables data to remain encrypted while supporting arithmetic computations. This enables secure and privacy-preserved machine learning (ML) on encrypted data. However, ML on encrypted data is significantly slower than on unencrypted data, indicating the need for more efficient implementations and software libraries. Recent studies indicate that data redundancy and unoptimized arithmetic in FHE libraries prevent broader FHE adoption. An innovative approach, Software-Defined Computer Arithmetic, addresses these issues through software-defined number formats and tailored arithmetic operations, promising significant improvements in FHE performance.
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
(14) 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
(15) PhD Degree – Fully Funded
PhD position summary/title: Side-channel analysis countermeasure of Quantum Safe implementations on hardware (aims to FPGA)
SCA is a real threaten to all cryptographic devices. Even though the conventional cryptographic algorithms like AES and the modern PQC algorithms like Latice based CRYSTALS-Kyber are proved safe in theory, their high-speed implementations on hardware (ASIC, FPGA) leaks sensitive information to side-channel through information like power consumption, electromagnetic field, and computation time. SCA applies divide and conquer strategy to find leakage of each small number of key bits group to side-channel while executing those data to find the whole secret key. Hence, SCA countermeasure implementations of PQC algorithm on hardware are necessary to increase the security. This project will:
[1] Identify the known and potential leakage points where sensitive data like the secret/private key, the message, data in key exchange mechanism can be revealed as well as attack models in PQC implementations in available hardware implementations, aiming to CRYSTALS-Kyber algorithm.
[2] Evaluate available PQC hardware implementation under SCA using conventional CPA or state-of-the-art ML based attacks.
[3] Apply hardware oriented SCA countermeasures methods like dual-rail logic, dual-rail memory, various multiplication schemes, masking, shuffling in implementing PQC on hardware and evaluate in FPGA.
[4] Re-evaluate the hardware implementations of PQC under SCA.
Deadline : 28 February 2025
(16) 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
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 with the help of machine learning or conventional correlation power analysis (CPA). Even though complex and high computational SCA countermeasure methods are applied, the implementations still leak sensitive information. This project utilize knowledge on the architecture and pipeline execution inside microcontroller to shuffle and mix up the execution of data at different time together to increase the safety of the implementation under SCA with less increase in computational complexity and power consumption.
Deadline : 28 February 2025
(17) 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
(18) 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
(19) 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
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(20) PhD Degree – Fully Funded
PhD position summary/title: Hardware Security for Approximate Computing
As IBM’s recent 2nm chip is pushing Moore’s law to its limit, conventional computing techniques are struggling to offer high performance computing within power consumption constraints. Approximate computing generates results that are good enough rather than always fully accurate. It was reported in 2018 as one of the top ten technologies that could change the world. Approximate computing offers up to an order of magnitude reduction in power consumption by accepting a margin of error in calculations, which reduces the accuracy of a specific result to within an acceptable threshold. Inspired by the fault tolerant capability of the human brain, approximate computing can accept errors in calculation without affecting the results of certain human perception and recognition related computation, including artificial intelligence (AI), deep learning (DL), machine learning (ML), signal processing and even some cryptographic schemes, in which noisy data, redundant information and inaccurate results are tolerable for the computation. Indeed, leading companies are undertaking research into potential products and services based on approximate computing, e.g., Google’s DL chip, IBM’s RAPID on-chip AI accelerators, etc. The biggest difference between accurate computing and approximate computing is the introduced errors in the result. An accurate computing design is supposed to generate precise results and any error, should it occur, would be unintentional. In contrast, an approximate computing design, by definition, may introduce errors and give many different results. However, a major threat to approximate computing is that the access control mechanisms for manipulating acceptable errors, which are added to designs to improve power consumption performance, provide a new attack vector over equivalent exact designs. This project will explore how to achieve secure and lightweight security designs for approximate computing. 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
(21) PhD Degree – Fully Funded
PhD position summary/title: Sub-Terahertz Antennas for 3D Heterogeneous Integration
With the escalating demand for high data rates, the currently utilized electromagnetic (EM) spectrum faces congestion, limiting data movement and creating bottlenecks in communications. To address these challenges, communication systems are expanding into higher frequencies, where the sub-terahertz-band (100 GHz to 300 GHz) offers large, untapped bandwidth and significant potential for high data-rate applications. However, operating within the band presents unique challenges due to the absence of sufficiently efficient RF electronics that meet the stringent size, weight, and power (SWaP) constraints required for compact systems. Current silicon technology suffers from low efficiency, making it crucial to develop advanced integration and packaging solutions to realize viable high-performance systems.
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.
Deadline : 28 February 2025
(22) 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
(23) 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.
The proposed design will investigate and optimize several antenna architectures to meet these objectives, exploring options like high-gain leaky-wave antennas for fast scanning capabilities, reconfigurable metasurface apertures for adaptive beam steering, and lens-based configurations for precise near-field focusing. By integrating these innovative antenna concepts, the project seeks to deliver:
1. Wide Field-of-View (FOV): An antenna system that enables large FOV imaging suitable for detecting objects at varying angles and positions, enhancing versatility for full-body scanning or expansive industrial monitoring applications.
2. High-Resolution Imaging: A design approach that prioritizes high-resolution, near-field imaging for short-range applications, allowing detailed visualization of objects with high accuracy.
3. Dynamic Adaptability: The incorporation of mechanisms for reconfigurable and adaptable focus adjustments, enabling the antenna system to maintain consistent imaging quality across different distances and angles.
By leveraging cutting-edge antenna architectures such as reconfigurable metasurfaces and leaky-wave designs, this project will create a versatile, high-resolution imaging solution tailored for the next generation of terahertz imaging systems. The anticipated outcomes include a prototype capable of efficient, real-time imaging at sub-millimeter wave frequencies, which will serve as a foundation for advancing terahertz imaging technology in security, healthcare, and industrial fields.
Deadline : 28 February 2025
(24) 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
(25) 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
(26) PhD Degree – Fully Funded
PhD position summary/title: Digital Twins for Empirical Elasticity between the Edge and Cloud
Over the last few years, Infrastructure as a Service (IaaS) has become widely available with Cloud providers such as Amazon, Microsoft and Google. These IaaS providers allow us to deploy services on a pay-as-you-go basis. However, the use of the machines of IaaS providers can be expensive for a long period of time and for a big number of end-users. Moreover, the latency of the communication between the end-users and the cloud machines can be high when the cloud machines are not located close to the end-users’ devices and when the cloud machines are frequently used. To reduce the usage cost and the communication of cloud machines, the Edge that includes devices at the network edge has been recently proposed. While there are approaches in the literature that combine Cloud and Edge machines, these approaches do not address the challenge of how the dynamic switching between the Edge and the Cloud can be achieved at runtime without suspending the execution of the deployed Web services/APIs on the machines.
Deadline : 28 February 2025
(27) PhD Degree – Fully Funded
PhD position summary/title: Managing Technical Debt in Research Software
Researchers who write code develop software where the end goal of the activity is not actually the software itself, but rather a tangible research output (e.g., a journal article, a conference paper etc.). If the software is made well, the software then also becomes a tangible research output, eligible for a Digital Object Identifier (DOI), publication and reuse to underpin other research [1]. Although researchers-who-write-code have evolved into Research Software Engineers (RSEs), the standard practice of employing publications to assess an individual’s academic performance has remain unchanged. This often means that RSEs must write software and also publish papers to prove their value under this credit system [2]. With the continued focus on assessing papers, and not assessing the software, the quality of the underlying software can be compromised- reducing its maintainability and sustainability and thus increasing its technical debt.
Deadline : 28 February 2025
(28) PhD Degree – Fully Funded
PhD position summary/title: Chatbot for Dynamic Data-Oriented Serverless Edge Computing
Edge computing can support Web services with fast response time and low bandwidth usage by moving computation from the Cloud to machines that are located close/on the Edge (i.e., close/at the end-users’ devices). The existing Edge computing frameworks support the design and the deployment of services that are deployed/bound to specific machines on the Edge. In this case, service developers should statically define/decide at the development time of their software what services should be deployed on what type of machines on the Edge. However, services may not be able to run on machines on the Edge that are usually resource constrained. The performance of services depends not only on their particular development details but also on their particular runtime use (e.g., dependence on the exchanged data) that cannot be known at the development time of the services. Thus, the project faces the challenge of redesigning/designing service-oriented software at runtime to make it deployable to the Edge
Deadline : 28 February 2025
(29) PhD Degree – Fully Funded
PhD position summary/title: Secure Synchronised Measurements for Smart Grids
Synchronised Measurements are a vital component to the operation of novel protection and control systems in Smart Grids. This project will investigate the optimal ways to implement this emerging technology, considering several areas including metrological aspects of power system phenomena, time synchronisation technologies through to secure data telecoms.
While synchrophasor measurement technology has been successful in the electrical transmission system, electrical distribution networks present many new measurement challenges. In particular, the highly distorted voltage and current waveforms do not lend themselves to expression as synchrophasors, and doing do loses information which is important to understanding system behaviour. The first stage of this project will be to conduct a study of such phenomena using the established ‘OpenPMU’ technology platform, and then implement measurement algorithms which target the signal attributes of interest, for example the harmonic phasors.
Subsequently, the project will turn focus to the IEC 61850 environment, and will study how the measurement algorithms developed may be implemented using professional substation equipment. In this part of the project, there is a need to consider the cyber security requirements as there will be a need for the data produced to traverse wide area networks, most often public networks such as the Internet.
Deadline : 28 February 2025
(30) PhD Degree – Fully Funded
PhD position summary/title: Machine Learning for Secure 6G Cell-Free Massive MIMO
This PhD project aims at designing novel signal processing with the aid of machine learning techniques to improve the security for cell-free massive MIMO systems, which is expected to have significant impact on the development of 6G wireless networks.
In conventional cellular wireless networks, a land area is divided into regular shaped cells (e.g., hexagonal, square, or circular). Each cell is served by one base station, and uses a different set of frequencies from neighboring cells, to avoid interference. By contrast, in this PhD project, we are targeting a disruptive and novel technology called “cell-free massive MIMO”. This novel technology has recently attracted a lot of attention from both academia and industry (e.g. Ericsson, Nokia Bell Labs, Huawei, etc.). In cell-free massive MIMO, a number of access points, which are distributed at random in a very wide area (e.g. over an entire city), serve simultaneously many users randomly distributed in the same area. This technological paradigm is expected to offer many advantages compared with the conventional wireless systems: 1) huge throughput; 2) huge energy efficiency; 3) and high coverage probability. Thus, cell-free massive MIMO is a disruptive technology for next generations of densified wireless systems.
Deadline : 28 February 2025
(31) PhD Degree – Fully Funded
PhD position summary/title: Machine Learning for Integrated Sensing and Communication
This PhD project aims to design novel resource allocation and signal processing methods using machine learning techniques to enhance the performance of both communication and sensing systems, which is expected to significantly impact the development of 6G wireless networks.
Integrated Sensing and Communication (ISAC) represents a significant advancement in future wireless systems, where both sensing and communication use the same frequency band and hardware. This approach is crucial for various important applications in fifth-generation (5G) and future networks, including autonomous vehicles, extended reality, and smart healthcare monitoring, which require robust sensing capabilities alongside effective wireless communications. For instance, in the context of autonomous vehicle networks, these vehicles will rely on the network to gather extensive data, such as ultra-high-resolution maps and near-real-time information, which are essential for navigation and for avoiding traffic congestion. Consequently, ISAC has attracted substantial attention and interest from both academic and industry. A main challenge of ISAC is the efficient design of an integrated system that optimally supports both sensing and communication functionalities.
Deadline : 28 February 2025
(32) PhD Degree – Fully Funded
PhD position summary/title: Enhancing the Parallel Scalability of Molecular Dynamics Simulations with Machine Learning-Based Prediction
The goal of this project is to investigate, design and evaluate algorithms for accelerating MD simulations based on the estimation or prediction of long-range field values in MD simulations. The algorithms are to minimize end-to-end execution time while minimizing the simulation error. The field values evolve non-linearly and it is an open question what models track this progression accurately. Machine learning models are broadly a feasible class of models and the aim is to identify suitable models and evaluate them in terms of their accuracy and inference overhead. A secondary direction of the research is to design mechanisms to assess the impact of predictions on simulation accuracy during any time step of the simulation. The purpose of this assessment is to ensure accuracy, where important deviations of accuracy can be compensated, e.g., by roll-back actions or by reducing frequency of applying predictions of the field values. These mechanisms should then be integrated in an MD simulation framework and evaluated for their robustness and improved accuracy.
Deadline : 28 February 2025
(33) PhD Degree – Fully Funded
PhD position summary/title: Causal Network Analysis for Compliance Management
Compliance Management (CM) refers to the process of ensuring that an organisation adheres to legal, regulatory, industry, and organisational standards and requirements. This involves developing, implementing, and maintaining policies, procedures, and practices that ensure the organisation operates within the bounds of applicable laws and regulations. Effective CM helps organisations mitigate risks, avoid legal penalties, and maintain a positive reputation. The Applied Research and Engineering Centre (ARC) in Queen’s has been working in CM since 2022, having developed tools for compliance checking based on obligation extraction and mapping.
Building on existing work on CM in ARC, this PhD project will investigate how causal network analysis can enhance CM in terms of efficiency and effectiveness. It is expected that causal network analysis can enable to (1) explain compliance status (to clarify why a company is compliant or non-compliant with specific regulations) (2) identify root causes (to pinpoint the underlying causes of non-compliance) (3) optimise compliance measures (to recommend cost-effective actions to achieve and maintain compliance) (4) determine compliant policies (to identify specific policies that ensure compliance with multiple regulations).
Deadline : 28 February 2025
(34) PhD Degree – Fully Funded
PhD position summary/title: High-Performance Graph Processing
The aim of this PhD project is to analyse and design efficient algorithms for hard graph analytics problems, aiming to shed light on one of these questions (each question could be a different PhD project):
1. How can we combine the goals of parallel scalability and energy-efficiency, given that increasing parallelism for an irregular computation has limited returns on efficiency?
2. How can we design architecture-aware algorithms that adapt the choice and parameterisation of the performance optimisations to the specifics of the memory hierarchy and coherence mechanism?
3. How can we apply algorithmic choice (selecting one of multiple algorithms that compute the same result but with different performance on relevant inputs) in the context of NP hard algorithms such as subgraph isomorphism and clique problems?
4. How can we trade accuracy of computation against reduced energy consumption and increased performance?
You will investigate novel algorithms and their implementation paying attention to both (theoretical) algorithmic techniques and their efficient implementation. Hereto, you can build on our prior research on graph processing, including the GraphGrind, LaganLighter and Graptor graph processing systems, and research results on clique and graph isomorphism problems.
Deadline : 28 February 2025
(35) PhD Degree – Fully Funded
PhD position summary/title: Signature Learning for Object Detection Using Attention Mechanisms
Object detection traditionally relies on segmentation to extract features for recognition, but this can be challenging in cluttered or occluded environments. This project aims to develop a signature learning approach for detecting objects directly from data (e.g., images or videos) without explicit segmentation. By incorporating attention mechanisms, the model will focus on relevant regions and features that characterise the object of interest, enhancing detection accuracy.
This project will develop an attention-based model for object detection that learns distinct object signatures directly from unsegmented data. Traditional object recognition methods rely heavily on segmentation to isolate objects within images or videos, which can be challenging in cluttered or occluded environments. This project aims to bypass segmentation by leveraging attention mechanisms that focus on the most relevant regions and features, enabling the model to recognize objects based on their unique “signatures.”
Deadline : 28 February 2025
(36) PhD Degree – Fully Funded
PhD position summary/title: Exploring Security and Privacy Risks of Large Language Models
This proposed PhD research aims to conduct an in-depth investigation into the security and privacy risks associated with large language models (LLMs). As the deployment of LLMs becomes increasingly prevalent across various domains, understanding and mitigating potential threats is paramount. The focus will be on studying jailbreaks, evasion from safeguards, and data privacy attacks, with the ultimate goal of proposing robust defensive strategies.
Deadline : 28 February 2025
(37) PhD Degree – Fully Funded
PhD position summary/title: Exploring Generative AI for Offensive and Defensive Cybersecurity: Risks and Opportunities in Malware Detection and Exploitation Generation
Objective 1: Analyze the risks and mechanisms by which code-generating LLMs could generate or support the discovery of zero-day vulnerabilities.
Objective 2: Develop methodologies for extracting cybersecurity-relevant information from code representations within LLMs, focusing on knowledge extraction, feature mapping, and pattern recognition.
Objective 3: Leverage LLMs to enhance malware detection by representing code and malicious activity in a hyperdimensional space, enabling new avenues for identifying and recognizing emerging threats.
Objective 4: Propose defense strategies to mitigate the misuse of generative models in offensive cybersecurity.
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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