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: Managing Technical Debt in Research Software
Aims:
• Investigate the concept and presence of technical debt within research software, where pressure for rapid publishable research results, together with limited time, effort and resources, can often lead to short-term “quick fixes” at the expense of long-term high-quality software.
• Propose and implement a framework for measuring, tracking, and managing technical debt in research software. Such a framework may be adapted from frameworks for technical debt in commercial software that focus on code quality, testing, and refactoring.
Deadline : 28 February 2025
(02) PhD Degree – Fully Funded
PhD position summary/title: AI for Adaptation and Evaluation of Quantum Positioning Systems for Autonomous Vehicles
AV Technology, also known as Automated Driving Systems or Self-Driving Vehicles, promises to transform future transport systems. AVs rely on AI-based environment perception systems and high-precision positioning systems to understand AVs’ surrounding world and their positions (for example, their GPS coordinates). QUB will adapt and test Quantum Positioning for AVs within the QEPNT hub in collaboration with the hub partners.
As a part of this hub, we invite highly motivated applicants to apply for two PhD positions at QUB to help us with the adaptation and evolution of Quantum Positioning Technology for AV. The successful candidates will have a unique opportunity to work on cutting-edge technology and collaborate with QEPNT hub’s academic and industry partners.
The research will focus on developing AI-based tools and digital twins to help QEPNT researchers adapt Quantum Positioning Technology for AVs. This will involve developing AI-based tools for integrity monitoring for Quantum Positioning Systems and developing a state-of-the-art digital twin in advanced simulation environments. The research will also include participation in developing experimental Proof of the Concept demonstrators, participation in field trials, and using AI for data analysis.
Deadline : 28 February 2025
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(03) PhD Degree – Fully Funded
PhD position summary/title: Transforming Healthcare: A Vision-Language Model for Medical Image Analysis and Report Generation
Chest pain and shortness of breath are one of the most frequent reasons for visiting hospitals and the emergency department. Radiological Imaging techniques such as X-ray provide valuable insight into the human body and allow doctors to visualize and find health-related complications. It is one of the most common types of examination for diagnosing pneumonia and pleural effusion. The accurate interpretation of X-ray images in a short time can be complex for inexperienced practitioners. An automated system could help identify possible complications, supporting doctors in their analysis and reducing the burden of interpreting these images. Ultimately, this would enhance the overall healthcare system and lead to better patient care.
Deadline : 28 February 2025
(04) PhD Degree – Fully Funded
PhD position summary/title: Citizen-Led Gen Ai Governance: Models, Tools and Practice
This research seeks to investigate citizen-led approaches to AI governance, with specific focus on Generative AI. Emphasis is on the design and development of digital tools that can support a values-first [2] participatory decision-making process where agreed values and principles pertaining the use and production of Gen AI can be mapped to concrete human and software behaviour [3].
Deadline : 28 February 2025
(05) PhD Degree – Fully Funded
PhD position summary/title: A light-weight deep learning model for detecting heart abnormalities
According to the world health organization (WHO), in 2021, around 32% deaths were caused by cardiovascular diseases. An early diagnosis can potentially save the millions of people and healthcare cost around the globe. The electrocardiogram (ECG) is non-invasive and low-cost method to monitor the heart muscle activity. The cardiologist used ECG bio signals for analysis to quantify the normal or abnormal heart rhythm known as arrhythmia. A scheduled clinical ECG can provide the information if cardiovascular disorder is present at the time of check-up and does not provide information about the rhythm in daily routines. With the advancement in wearable technology, such as smartwatch has the ability to capture the real-time ECG continuously. Such devices can be utilized as a screening tool to capture the heart muscle activity at large scale and intelligently process the data using machine learning models. In traditional approaches, ECG signal are processed in frequency domain to label the presence of arrhythmia. Recently, machine learning models, especially deep learning algorithms achieved a remarkable improvement to solve the complex learning tasks of medical image analysis as well as in precision medicine. These models can be foreseen to predict the potential risk of cardiovascular disorder. These models consist millions of parameters to train as well as make inference. It limits the applicability inside the wearable devices and pose a challenge on the energy consumption. To solve this challenge, knowledge distillation can play an important role. In knowledge distillation a trained deep learning model capability is transferred to a smaller neural network for resource-constrained devices. Such light-weight models can easily process the massive data inside the wearable devices.
Deadline : 28 February 2025
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(06) PhD Degree – Fully Funded
PhD position summary/title: Empowering Mothers: AI-Enhanced Personalized Management of Gestational Diabetes
This PhD project proposes the development of an AI-powered platform to enhance the management of Gestational Diabetes Mellitus (GDM). The system will focus on early detection, continuous monitoring, and personalized interventions by leveraging machine learning and predictive modelling. By identifying key risk factors and predicting complications, the platform will enable timely and tailored care, offering tools for blood glucose monitoring, dietary recommendations, and insulin management. It will also provide educational resources, ensuring women—especially those from underserved groups—are empowered to manage their condition effectively. This project aims to address critical gaps in GDM care, improving maternal and neonatal outcomes and reducing long-term health risks.
Deadline : 28 February 2025
(07) PhD Degree – Fully Funded
PhD position summary/title: Breaking Barriers: AI-Powered Sign Language Translation for Enhanced Healthcare Access for the Deaf
This PhD project proposes the development of an AI-powered sign language translation system to bridge communication gaps for Deaf individuals in healthcare. The proposed solution will focus specifically on appointment booking and patient and healthcare professionals, including doctors’ communication by enabling real-time translation between text and BSL. By leveraging advancements in visual recognition for sign language and natural language processing, the system will support two-way translations, enabling sign-to-text and text-to-sign interactions. This accessible, real-time translation tool will enable Deaf individuals to independently navigate healthcare processes, fostering a more inclusive and responsive environment where Deaf patients can access care equitably. Through this initiative, the project aims to address a critical accessibility gap, helping ensure that Deaf individuals receive timely, effective, and compassionate healthcare.
Deadline : 28 February 2025
(08) PhD Degree – Fully Funded
PhD position summary/title:
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.
Deadline : 28 February 2025
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(09) 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.
Deadline : Open until filled
(10) 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.
Deadline : Open until filled
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(11) 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
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(12) PhD Degree – Fully Funded
PhD position summary/title: High-Performance Graph Processing
Many disciplines rely heavily on the efficient analysis of graph-structured data, e.g., bio-informatics and computational genomics, cybersecurity, epidemiology, biology, and social sciences. Graphs can become very large, prompting the use of clusters of computers to achieve fast turn-around times. However, clusters pose multiple challenges to efficient computation, in particular for graph analytics. Very often, graphs have skewed degree distributions, making it hard to partition them properly across the nodes of the cluster. Poor partitioning inflates network communication volume, increases workload imbalance and potentially results in redundant work. Graph analytics are very often communication-bound and spend significant time waiting on network communication to complete. By consequence, constructing fast and energy-efficient graph analytics algorithms with good parallel scalability is non-trivial.
Deadline : 28 February 2025
(13) 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.
Deadline : 28 February 2025
(14) 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
(15) 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, including security-sensitive 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 towards safe LLMs.
Deadline : 28 February 2025
(16) 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
(17) PhD Degree – Fully Funded
PhD position summary/title: Precomputation Attack Resilient Password based Authentication System Design
Resiliency and Survivability at the face of real world cyber attack are in high demand as customers are demanding system designs that assure continued safety and functionality during and after systems are compromised by cyber attacks. In the context of password based authentication systems, resiliency and survivability at the face of server compromise is a big challenge and no existing scheme can withstand the damage once the server is compromised. The project investigate password authenticated key exchange (PAKE) mechanism of authentication and provide commercially viable solution that can withstand the attack followed by server compromise so as to uphold resiliency and survivability.
Deadline : 28 February 2025
(18) PhD Degree – Fully Funded
PhD position summary/title: Secure Record Linkage
Record linkage is the technique which aims to link the records from two or more distinct sources. This has been commonly adopted in biomedical informatics to link electronic health records (EHRs) of the same patient in different medical institutions, e.g., for building the longitudinal health profile of the patient or for de-duplicating individual records in multiple databases, etc. Due to the concerns of patients’ confidentiality and/or the institutional data sharing policy, the patient records, in particular the identity keys (e.g., the patients’ name, SSN, address, etc.), cannot be directly shared with the other party. Therefore, a secure algorithm is needed to achieve record linkage while the protected keys are only shared in their encrypted form.
Deadline : 28 February 2025
(19) PhD Degree – Fully Funded
PhD position summary/title: Secure searching over sensitive data in a multi-client setting
This project will investigate a novel searchable encrypted sensitive data like e-medical framework for multi-client which provides both confidentiality and searchability. The project will focus on both the public key setting and the symmetric key setting depending on the different usecases. Also the project investigates on enabling the secure data update of the encrypted database by leveraging a secure dynamic searchable encryption.
Deadline : 28 February 2025
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(20) PhD Degree – Fully Funded
PhD position summary/title: Self-supervised summarisation of hyperspectral image data
Hyperspectral imaging is an important technology in medicine, biosciences, materials sciences, remote sensing, astronomy, chemical analysis, and many other areas. Whilst the cameras that we are familiar with from day-to-day use acquire images that have three colour channels (Red, Green, Blue, or RGB), hyperspectral imagers acquire images with tens, hundreds, thousands, or even millions of colour channels. The images they acquire can reveal the world around us in tremendous details, for example, by allowing the chemical composition of an object to be understood at the molecular level, or by identifying subtle differences in the “colour” of vegetation that might allow the fertility of farmland (for example) to be understood from satellite images. The tremendous information content of hyperspectral image comes at a cost: the images can require tens to hundreds of gigabytes to store, and not only is this practically challenging, it is very hard indeed to properly examine all of the data. It would be highly beneficial to be able to both reduce the digital size of the data, and to reduce its complexity so that it can be more properly examined by those who wish to use it. Many techniques have been used to achieve this, for example, principal component analysis (PCA) can be used to project the data into a low-dimensional space for easier analysis. This is often useful, but breaks down when the data has a highly nonlinear structure. Other, more sophisticated techniques such as t-SNE and UMAP can be used to generate low-dimensional visualisations of the data that can reveal some patterns, but which tend to severely distort the data and to hallucinate structure that isn’t really there. In this project we will develop techniques for summarising very high dimensional hyperspectral imaging data. Our goal will be to generate a single “summary” image that enables the image content to be rapidly visualised in a controllable way. Given the extreme complexity of the data, we cannot rely on labelled data and so we will approach this using self-supervised learning approaches to determine what the salient features in the images are, and to embed them into a single image space that permits a simple and interpretable visualisation of the data.
Deadline : 28 February 2025
(21) PhD Degree – Fully Funded
PhD position summary/title: Learning to engineer drugs
In this project we will develop novel machine-learning approaches to identify the fine-grained interactions that control the effects of drugs. We will use a dataset derived from a combination of publicly available data and computational docking poses to learn the complex relationships between the many interdependent variables and incorporate our existing knowledge of molecular structure and function. We will develop new methods to learn specific features of a protein residue that can be used to predict the receptors contribution to signalling. We will also incorporate molecular structural information to generalise predictions to other drug targets. The project will involve a range of machine learning techniques such as graph neural networks which have been shown to be effective at representing molecular structures, and potentially novel applications of language models that have been shown to be effective in predicting molecular properties.
Deadline : 28 February 2025
(22) PhD Degree – Fully Funded
PhD position summary/title: Retinomorphic Event-Driven Computer Vision
This project addresses efficient real-time processing of event streams using programmable hardware such as Field Programmable Gate Array (FPGA). A key series of operations need to be performed, including activity filtering to separate valid events from noise, along with movement detection and other image processing tasks. Data rates can reach billions of events per second and so high speed digital circuitry is required to support real-time processing.
Deadline : 28 February 2025
(23) PhD Degree – Fully Funded
PhD position summary/title: Edge Intelligence: Neuro-Optimisers on the Edge
In this project, we will design, implement, and evaluate a new class of AI/ML-based techniques, called Neuro Combinatory Optimisers (NCOs), to perform real-time analysis or decision-making processes on edge devices. NCOs combine neural networks with advanced optimisation techniques to create more robust and flexible systems capable of handling uncertainty in dynamic environments.
Deadline : 28 February 2025
(24) PhD Degree – Fully Funded
PhD position summary/title: Neuro Combinatory Optimisers (NCOs) for Cloud Computing Platforms
In this project, we aim to design NCOs to address continually evolving complex challenges in cloud computing environments. NCOs learn from historical cloud data and adapt to changing cloud conditions to optimise multiple, sometimes conflicting, objectives such as performance vs. energy efficiency in large-scale clouds. NCOs will be designed with respect to their computational overhead, data requirements, scalability, and interpretability, along with their true real-time performance and robustness in optimising large-scale clouds hosting complex resource-constrained multi-component services. All proposed solutions will be empirically evaluated in real environments.
Deadline : 28 February 2025
(25) PhD Degree – Fully Funded
PhD position summary/title: 3D human activity recognition and understanding using mmWave radar
Human activity recognition is a critical research area with wide-ranging applications in healthcare, robotics, virtual reality, and sport analysis. Numerous contactless solutions have been developed to achieve accurate and robust human activity recognition, such as computer vision, multiview and multimodal learning paradigms. However, each of these methods has limitations, including dependence on lighting conditions, occlusion challenges, and the need of extensive calibration.
With the rapid advancement of IoT, wireless sensing has gained increasing attention due to its ability to operate in low-visibility environments such as dense fog, smoke, snow-storm, and rain, while also offering better privacy protection compared to computer vision-based methods. Among wireless sensing modalities, millimeter wave (mmWave) radar stands out due to its high-resolution sensing capabilities, surpassing alternatives like Wi-Fi and RFID. By extracting and analysing point clouds generated from mmWave radar reflections off the human body, it becomes possible to achieve contactless human activity recognition. Moreover, advancements in artificial intelligence (AI) have the potential to greatly enhance the performance of mmWave radar-based sensing systems.
This PhD project aims to develop an AI-assisted mm-wave radar sensing system to perceive and understand human activities in a contactless and passive manner. By addressing the technical challenges in signal processing, feature extraction, real-time modelling and application-specific adaptation, the project has the potential to contribute significantly to the field of contactless human activity recognition.
Deadline : 28 February 2025
(26) PhD Degree – Fully Funded
PhD position summary/title: Contactless healthcare monitoring for multi-environment adaptability
Radio frequency-based contactless sensing has attracted significant attention in recent years, driven by the ubiquitous deployed IoT infrastructures and the advancement of wireless communications technologies. Unlike conventional sensing modalities, radio frequency-based contactless sensing does not require the entities to equip with any on-body sensors, instead, it leverages the signal distortions and machine/deep learning for various sensing tasks. Moreover, such set of approaches is particularly effective in poor illumination and obscured environments without any practical inconvenience and privacy leakage.
This PhD project will focus on healthcare monitoring applications using the ubiquitous Wi-Fi signals to monitor respiration under environmental changes, employing advanced artificial intelligence (AI) and signal processing techniques.
One of the key challenging issues is the heterogeneity of wireless data. The collected data can exhibit varying statistical characteristics due to various uncertainties such as multipath propagation, hardware imperfections, and interference from nearby individuals. This variability can lead to pattern inconsistency, resulting in degraded performance. By addressing this challenge, the project aims to explore the limits of wireless sensing, enlarge the sensing range, and improve the accuracy, robustness and generalization of contactless healthcare monitoring systems.
Deadline : 28 February 2025
(27) PhD Degree – Fully Funded
PhD position summary/title: AI-enhanced next-generation health sensing using mmWave radar
Continuous monitoring of vital signs, such as respiration, heartbeat, heart rate variability (HRV), blood pressure (BP), electrocardiogram (ECG), and seismocardiography (SCG), is essential for early detection and prevention of potentially life-threatening conditions. However, existing solutions often require users to wear dedicated devices like wrist-worn sensors or chest straps, which can be uncomfortable and may cause skin allergies. With the rapid development of IoT and wireless communication technologies, wireless sensing has emerged as a promising alternative. This approach takes advantage of the ubiquitous presence of wireless devices and the fact that the presence of human body can alter wireless signal propagation, enabling contactless and passive monitoring of human’s vital signs through the analysis of the electromagnetic wave. This innovation offers a more convenient and non-invasive way to monitor health, making it a valuable tool in medical and wellness applications.
Deadline : 28 February 2025
(28) PhD Degree – Fully Funded
PhD position summary/title: Scalable Wi-Fi-based localization and trajectory tracking with AI
This PhD project aims to explore the use of commodity Wi-Fi devices for real-time target localization and trajectory tracking. By leveraging state-of-the-art signal processing and machine/deep learning techniques, the project will seek to enhance the accuracy, robustness and scalability of Wi-Fi-based localization and tracking systems. The project offers a unique opportunity to contribute to cutting-edge wireless sensing research, and collaborate with leading experts in AI, wireless technology and signal processing.
Deadline : 28 February 2025
(29) PhD Degree – Fully Funded
PhD position summary/title: Creating Synthetic Data and Data Augmentation for Cyber Security Applications using AI
The research will aim to:
• Investigate and explore published literature in how to use original data samples to generate much larger volumes of synthetic data quickly, and in a reconfigurable way that allows multiple variations of data to be generated. A key question to explore is how much real data needs to be sampled and provided as an input to be able to produce meaningful synthetic data as an output.
• Investigate how AI models can be trained to generate data that is time-synchronised across multiple sources, such as network packet captures, firewall logs, host logs, etc. with specific characteristics or patterns, useful for specific cyber security testing scenarios. E.g. this might mean generating specific network data and sensor logs for a set of water pump controllers across and industrial control network over a very large synthetic time period, or a large volume of synthetic Active Directory or VPN logs representing the behaviour of thousands of users in a corporate IT network.
• Investigate and identify appropriate generative approaches for the goals above, considering, for example, commercial GPT or custom LLM-based models, as well as approaches such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), etc., as seen in recent literature.
Deadline : 28 February 2025
(30) PhD Degree – Fully Funded
PhD position summary/title: Adaptive Virtual Reality
The research program focuses on developing machine learning-enhanced VR systems that can process real-time psychophysiological measures and behavioural data. The system will operate through a closed-loop architecture that continuously monitors user state, analyses data patterns, and adapts the virtual environment accordingly.
Deadline : 28 February 2025
(31) PhD Degree – Fully Funded
PhD position summary/title: Autonomous Security for PLC Networks
Programmable Logic Controllers (PLCs) are embedded industrial computers used to automate manufacturing processes, assembly lines, and other equipment by continuously monitoring inputs, executing programmable logic, and controlling analogue or digital outputs in real-time. In recent years the convergence of IT and OT (Operation Technology) networks exposes PLCs to new threats that were not traditionally considered in such environments. PLCs have faced increasing security threats, exemplified by malware such as Incontroller (also called Pipedream https://www.dragos.com/blog/analyzing-pipedream-results-from-runtime-testing/). These sophisticated threats aim to exploit functions or vulnerabilities in PLCs to manipulate and disrupt industrial processes. To protect PLC networks, rules and policy-based approaches are relatively well established for monitoring the presence of some of the activities associated with cyber threats, such as attempts to connect via disabled services, or communication via unused ports and protocols. However, alternatively, at the device level, recently the ISA Global Cybersecurity Alliance (ISAGCA) proposed “20 Secure PLC Coding Practices” to define best security practices for PLC programmers, based on input from industry leaders and experts (https://plc-security.com). These are principally based on utilising existing functionality within PLCs, and the principles are applied at the design stage. Yet, there is unexploited potential to adapt many of the 20 principles to define features and a framework towards training models of how individual or groups of PLCs behave, with the objective to detect violations of the principles during run-time operations. Furthermore, since PLCs operate in environments where traditional IT security measures may not be feasible or could interfere with real-time control requirements, a further valuable research challenge is to consider how edge-based response agents might be able to intervene to recover an attacked PLC process into an acceptable and stable condition.
Deadline : 28 February 2025
(32) PhD Degree – Fully Funded
PhD position summary/title: Enhancing Chronic Disease Prediction Through AI-Driven Clinical Information Extraction from Electronic Health Records
The project aims to develop hybrid solutions by utilising linguistic methods and deep learning-based algorithms for the extraction of useful information from unstructured free text data and provide early diagnosis for certain chronic clinical medical conditions. It will enable the clinicians to better understand the development of various chronic diseases. A further objective will be sought to link the medical NLP analysis to other sources of data (e.g., patients’ physiological data) using appropriate data fusion algorithms for heterogeneous data sets.
Deadline : 28 February 2025
(33) PhD Degree – Fully Funded
PhD position summary/title: Utilising Vision-Language Models for Remote Sensing Analysis
This PhD research aims to explore how vision-language models, like CLIP, improve remote sensing interpretation by integrating visual and textual data. This project will investigate CLIP’s potential in extracting insights from remote sensing data for environmental monitoring, disaster management, urban planning, and applications related to monitoring climate change and global energy. Objectives are as follows:
1. Investigate existing vision-language models and their application to remote sensing analysis.
2. Develop novel methodologies to adapt vision-language models for remote sensing tasks, including feature extraction and semantic understanding.
3. Explore the integration of vision-language models with existing remote sensing analysis techniques to improve performance and efficiency.
Deadline : 28 February 2025
(34) PhD Degree – Fully Funded
PhD position summary/title: Active Polarisation Agile Spherical Retrodirective Antenna
The project will develop a very modern skills set, involving ultimate state of the art antenna design and experimental characterisation in our world class anechoic chambers at the Centre for Wireless Innovation, as well as the development and programming of its control electronics. At the end of the programme the candidate will have developed and characterised the world’s first fully adaptive retrodirective array along with its digital twin.
[1] Develop Circularly polarised antenna element
[2] Map antenna element onto sphere and simulate performance using an industry standard simulator
[3] Experimentally characterize Antenna Array properties
[4] Develop programmable control electronics
[5] Build demonstrator and test
[6] Publish papers and attend international conferences
Deadline : 28 February 2025
(35) PhD Degree – Fully Funded
PhD position summary/title: Advanced Reconfigurable Beamforming Hardware at Millimeter-waves
The PhD project aims to study advanced antennas and beamforming technology suitable for mmWave wireless communications. The proposed PhD thesis will be carried out in the state-of-the-art fabrication and measurement facilities, a part of the Centre for Wireless Innovation (CWI), located in the Titanic Quarters campus. Specifically related to the project, the Keysight Lab hosts a suite of advanced measurement equipment that will allow the doctoral candidate to design advanced antennas and characterise / model the mmWave radio characteristics to demonstrate a complete beamformer hardware platform operating in the mmWave spectrum.
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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