Recently, there has been growing interest in the use of deepfake AI to improve healthcare. This exciting project aims to leverage deepfake AI technology to enhance the development and evaluation of hearing-aid speech processing algorithms. By combining cutting-edge advances in conversational AI and speech processing, we aim to create a uniquely diverse and representative dataset that accurately simulates real-world conversational scenarios. Academic qualificationsA first degree (at least a 2.1) ideally in Computer Science/Engineering or a related area, with a good fundamental knowledge of machine learning, computer vision/graphics and/or statistical modelling.English language requirementIELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University's policy are available online.Essential attributesExperience of fundamental machine learning, computer vision/graphics, large language models and/or statistical modelling (or a related field), and proficiency in at least one programming language, such as Python, C#Competent in writing quality research papersKnowledge of quantitative research skillsGood written and oral communication skillsStrong motivation, with evidence of independent research skills relevant to the projectGood time managementDesirable attributesAbility to generate and implement new ideas.Knowledge/background in audiology, signal processing, AR/VR and/or behavioural science.Experience of qualitative research skills including experiments with human subjects.Ability to contribute to preparation of research funding proposals.Ability to work as part of an interdisciplinary team with endusers and researchers from different disciplines.Application processFor informal enquiries about this PhD project, please contact Professor Hussain at [email protected] or [email protected] application must include: Research project outline of 2 pages (list of references excluded). The outline may provide details aboutBackground and motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.Research questions orMethodology: types of data to be used, approach to data collection, and data analysis methods.List of referencesThe outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.Statement no longer than 1 page describing your motivations and fit with the project.Recent and complete curriculum vitae. The curriculum must include a declaration regarding the English language qualifications of the candidate.Supporting documents will have to be submitted by successful candidates.Two academic references (but if you have been out of education for more than three years, you may submit one academic and one professional reference), on the form can be downloaded here.Applications can be submitted here.