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Vacancies

Exciting opportunities for PhD candidates in three main areas. For more information email fani.deligianni@glasgow.ac.uk

SEMI-SUPERVISED MEDICAL IMAGE SEGMENTATION AND REGISTRATION

Research fields: Semi-supervised learning; Medical image analysis; Deep learning
Description: Medical image segmentation and registration are crucial tasks in healthcare diagnostics and treatment planning. However, these tasks often require large amounts of labelled data, which can be expensive and time-consuming to obtain. To address this challenge, this research focuses on developing semi-supervised learning techniques for medical image segmentation and registration that can effectively leverage both labelled and unlabelled data. By combining the strengths of supervised and unsupervised learning, we aim to reduce the dependence on large, annotated datasets while maintaining high accuracy.
Enrolment and opportunity: The successful candidate will enrol as a PhD student at the School of Computing Science under the supervision of Dr. Fani Deligianni and will join the Biomedical AI and Imaging Informatics Group. Our lab explores several research problems in semi-supervised learning, medical image analysis and deep learning. The candidate will have the opportunity to work on cutting-edge research at the intersection of machine learning and medical imaging with potential real-world applications in healthcare.
Skills: The ideal candidate will have a strong background in computer science, with a focus on machine learning and image processing. A solid foundation in mathematics or statistics is important. Special areas of interest include deep learning architectures for image analysis, semi-supervised learning techniques, and optimization methods. A good understanding of medical imaging modalities (e.g., MRI, CT, ultrasound) will be a considerable plus. Strong programming skills (Python, PyTorch or TensorFlow) are required. Experience in image processing libraries (e.g., OpenCV, SimpleITK) is desirable. Good communication skills in English and teamwork capacity are essential for collaborating with interdisciplinary teams and disseminate research findings.

PRIVACY-PRESERVING HUMAN MOTION ANALYSIS USING WIFI DATA

Research fields: Privacy-preserving machine learning; Human motion analysis; WiFi sensing; Deep learning
Description: Human motion analysis via WiFi sensing offers a non-intrusive method for monitoring activities in various settings, from healthcare to smart homes. However, the collection and analysis of such data raise significant privacy concerns, as WiFi signals can potentially reveal sensitive information about individuals’ behaviours and routines. This research focuses on developing privacy-preserving techniques for human motion analysis using WiFi data, ensuring that valuable insights can be extracted without compromising individual privacy. The project will explore advanced privacy-preserving machine learning techniques, such as differential privacy and knowledge distillation adapted specifically for WiFi-based motion analysis. By developing methods that can learn from distributed data sources without centralizing raw data, we aim to enable robust motion analysis while keeping personal data locally stored. Additionally, we will investigate techniques to minimize the risk of inverse attacks and ensure that the learned models do not inadvertently leak sensitive information.
Enrolment and opportunity: The successful candidate will enrol as a PhD student at the School of Computing Science under the supervision of Dr. Fani Deligianni and will join the Biomedical AI and Imaging Informatics Group. Our lab explores cutting-edge research problems at the intersection of privacy-preserving machine learning and human motion analysis. The candidate will have the opportunity to work on innovative solutions that balance the utility of motion analysis with stringent privacy requirements, potentially impacting fields such as healthcare monitoring, smart home technologies, and privacy-aware ambient intelligence.
Skills: The ideal candidate will have a strong background in computer science or electrical engineering, with a focus on machine learning and/or signal processing. A solid foundation in mathematics or information theory is important. Special areas of interest include privacy-preserving machine learning techniques (e.g. differential privacy) and deep learning architectures for time-series data, and WiFi signal processing. Familiarity with human motion analysis or activity recognition will be a considerable plus.
Strong programming skills (Python, PyTorch or TensorFlow) are required. Experience with privacy-preserving libraries (e.g., Opacus) and signal processing tools is highly desirable. Excellent communication skills in English and the ability to work in interdisciplinary teams are essential for collaborating on complex privacy-utility trade-offs and disseminating research findings to both technical and non-technical audiences.

AI-DRIVEN CLINICAL DECISION SUPPORT SYSTEMS USING ECG AND MULTI-MODAL DATA

Research fields: Clinical decision support systems; Multi-modal machine learning; Electrocardiogram (ECG) analysis; Explainable AI, Deep learning
Description: Clinical decision support systems (CDSS) have the potential to significantly improve patient outcomes by assisting healthcare professionals in making timely and accurate diagnoses. This research focuses on developing advanced AI-driven CDSS that leverage electrocardiogram (ECG) data alongside other multi-modal clinical information to enhance diagnostic accuracy and disease prevention. The project aims to create novel deep learning architectures capable of integrating and analyzing diverse data types, including ECG signals, patient demographics, laboratory results, and medical imaging. By fusing these multi-modal inputs, we seek to capture a more comprehensive view of a patient's condition, leading to more accurate and personalized diagnoses. A key challenge lies in effectively combining these heterogeneous data sources while maintaining interpretability of the AI's decision-making process. Furthermore, this research will explore the development of explainable AI techniques specifically tailored for clinical applications. This focus on interpretability is crucial for building trust among healthcare professionals and ensuring that the CDSS can provide not just predictions, but also clear, actionable insights into the underlying factors driving those predictions.
Enrolment & opportunity: The successful candidate will enrol as a PhD student at the School of Computing Science under the supervision of Dr. Fani Deligianni and will join the Biomedical AI and Imaging Informatics Group. Our lab investigates cutting-edge problems in AI for healthcare applications, including ethical considerations and explainable AI. The candidate will have the opportunity to work with multidisciplinary teams of both computing sciences and clinicians, as well as to develop innovative solutions that have the potential to directly impact patient care and clinical practice.
Skills: The ideal candidate will have a strong background in computer science or biomedical engineering, with a focus on machine learning and signal processing. A solid foundation in mathematics and statistics is essential. Special areas of interest include deep learning architectures for time-series and multi-modal data and explainable AI techniques. Familiarity with ECG interpretation and general medical knowledge will be a considerable plus.
Strong programming skills (Python, PyTorch or TensorFlow) are required. Experience with biomedical signal processing libraries is highly desirable. Knowledge of clinical workflows and regulatory requirements for medical AI systems would be beneficial. Excellent communication skills in English and the ability to work in interdisciplinary teams are essential for collaborating with healthcare professionals and translating complex technical concepts for clinical audiences