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Vacancies

Exciting opportunities for PhD and postdoc candidates in the following main areas. For more information email your CV to fani.deligianni@glasgow.ac.uk

CAUSAL SURVIVAL MODELS FOR PSYCHOSIS (Research assistant/Associate opening coming soon.)

Clinical Collaborators: Dr. Samuel Leighton and Dr Rajeev Krishnadas
Research fields: Machine Learning; Causal Inference; Health Data Science
Description: This project aims to develop innovative causal inference methodologies to elucidate the complex relationships between antipsychotic treatments, therapeutic efficacy, and side effects in patients with psychosis. We will develop causal survival models that accurately estimate time-to-event outcomes under different treatment scenarios while accounting for time- varying confounding and competing risks; and establish a methodology for reliable causal inference from observational psychiatric data that can inform clinical decision-making.
We are going to exploit survival models for a Scottish population with psychosis, leveraging approximately 10 years of data from over 2000 patients. In particular, we are going to use linked data from Esteem service on West of Scotland Safe Haven. This should be up to 2000 patients with linked labs and EHR data from secondary care, along with clinical reports of symptoms. Our primary objective is to understand the time-dependent risk profiles associated with different antipsychotic treatments, examining both therapeutic efficacy and physical health.
Enrolment and opportunity: The successful candidate will join the Biomedical AI and Imaging Informatics Group in the School of Computing Science at the University of Glasgow. Our research group tackles challenging problems in AI-supported clinical decision systems, working with multimodal data and electronic health records. This position offers exceptional opportunities to: (1) Conduct cutting-edge research at the intersection of causal inference, machine learning, and clinical psychiatry (2) Work with real-world healthcare data addressing critical questions in mental health treatment (3) Collaborate with clinicians, statisticians, and data scientists in an interdisciplinary environment (4) Develop methodologies with direct clinical impact and translational potential (5) Access state-of-the-art computational resources and a vibrant research community
Skills: The ideal candidate for the available research assistant position would be someone with an interest in continuing for a PhD starting in fall 2026 or a PhD candidate already submitted their thesis in a related topic. The candidate should have a strong background in computer science, with a focus on machine learning and/or statistics. A solid foundation in mathematics or statistics is important.

PRIVACY-PRESERVING DEEP LEARNING MODELS IN COMPUTER VISION - 2025

(PhD Scholarship opportunities can be found https:www.gla.ac.ukpostgraduateresearchcomputing)

Research fields: Privacy-preserving machine learning; Deep learning; generative models
Description: This project will develop privacy-preserving deep learning models with formal guarantees for computer vision applications. While WiFi radar sensing offers a non-intrusive method for monitoring activities in healthcare and smart home environments, the collection and analysis of such data raise significant privacy concerns, as signals can potentially reveal sensitive information about individuals’ behaviours and routines. The research will advance adversarial representation learning techniques that provide formal privacy guarantees for both generative models and downstream computer vision tasks. We will develop novel architectures that learn privacy-preserving representations through adversarial training, where privacy-sensitive attributes are actively suppressed while task-relevant information is preserved. The project will establish theoretical foundations and formal guarantees for these methods, including differential privacy bounds and information-theoretic privacy measures. Key research directions include: (1) adversarial representation learning frameworks with provable privacy guarantees that are robust against adversarial samples; (2) privacy-preserving generative models that can synthesize realistic motion patterns without leaking individual-specific 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 research group tackles cutting-edge problems at the intersection of privacy-preserving machine learning, computer vision, and healthcare applications. This position offers exceptional opportunities to: (1) Develop novel adversarial learning frameworks with formal privacy guarantees for computer vision systems (2) Advance the theoretical foundations of privacy-preserving generative models and representation learning (3) Work on real-world applications in healthcare monitoring and privacy-aware ambient intelligence (4) Collaborate with experts in machine learning theory, computer vision, and privacy-preserving systems (5) Contribute to research with significant impact on privacy-critical domains, from smart homes to clinical settings (6) Publish in top-tier venues for machine learning, computer vision, and privacy research
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. Familiarity with computer vision projects such as face recognition, 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.

SEMI-SUPERVISED MEDICAL IMAGE SEGMENTATION AND REGISTRATION - 2024

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 - 2024

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 - 2024

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.