Motor Neurone Disease (MND) is a rapidly progressing neurodegenerative disease that causes muscles to weaken and waste. It is a life-shortening disease for which there is limited treatment and no cure. Currently, detecting fasciculations is done by sticking needles into the muscles, which can be painful and unpleasant. However, there is a way of ‘seeing' fasciculations using ultrasound imaging. Previous work applied foreground detection-based image analysis approaches to detect fasciculations in b-mode ultrasound videos, showing that fasciculations look different in healthy and MND-affected muscles. Further work is needed to develop ultrasound image analysis into a clinically useful tool for early MND diagnosis and treatment monitoring. This opportunity involves a fully funded fixed term research associate position to advance ultrasound image analysis tools. The project includes collaboration with researchers and clinicians at Manchester Metropolitan University, King's College London, and University College London to provide a sensitive and pain-free way of monitoring muscle health in MND, enabling early diagnosis and new treatment discoveries. The Role: The role involves advancing biomedical image analysis and interpretation tools to improve measurement and monitoring of muscle health in people living with MND. This includes computational analysis of ultrasound images from people with MND and healthy controls. The work will utilize Gaussian mixture models for foreground detection, aiming to build, test, and refine data models that can identify MND sensitively and discriminate between disease stages. We seek someone with experience in biomedical image analysis, statistical modelling, machine learning classification, and data visualization. Excellent coding skills in a suitable language are required, with knowledge of Python and MATLAB beneficial. Experience with clinical data and industry collaboration is desirable. Other Duties and Responsibilities Include: Maintain accurate records of research conducted and carry out analysis of the results obtained using the most appropriate method. Work independently and in conjunction with other investigators and collaborators. Prepare research findings for publication and presentation. Write and co-ordinate applications for research grant funding. Assist in the supervision of postgraduate students, PhD students, and Technicians. Participate in the activities of the research group via meetings and seminars. Support dissemination of findings through delivery of workshops with potential stakeholders, including clinicians and industry. You will work within the Department of Life Sciences and Department of Computing and Mathematics, which provide state of the art facilities in the Dalton building. The Faculty of Science and Engineering offers a dynamic environment for cutting-edge research. Qualification Required: Hold a PhD in relevant computational or data science subjects. Application Requirements: Experience of the following would be advantageous: Working with biomedical images, particularly b-mode ultrasound image sequences. Computational image analysis, including foreground detection. Data modelling from logistic regression to AI-based machine learning for classification. Data visualization approaches, especially for non-technical audiences (e.g., clinicians, patients, carers). Proficiency in statistical evaluation. Publication in peer-reviewed journals relevant to the field. To Apply: Please submit your CV and Cover Letter. For an informal discussion, please contact Prof. Emma Hodson-Tole (e.tolemmu.ac.uk). Manchester Metropolitan University fosters an inclusive culture of belonging that promotes equity and celebrates diversity. We value a diverse workforce for the innovation and diversity of thought it brings and welcome applications from underrepresented communities.