Disease estimates of years lost to disability, where alopecia areata (AA) was ranked higher than both psoriasis and melanoma in disease impact. Before significant diffuse hair thinning becomes clinically appreciable, over 50% scalp hairs need to be lost. Therefore, the process may have been occurring for many months or years before someone presents to their doctor. Current treatments are reasonable at stabilising hair loss but not very good at reversing it. Therefore, being able to identify the problem and treat early in the course of the disease would result in better outcomes for the patient. We are looking for candidates to research computer vision and artificial intelligence techniques to create an objective method of measuring areas of hair loss/recovery. This will tackle the problems of current technology, where it can be time-consuming, expensive, or require shaving of a patient’s hair. The successful candidate will benefit from training facilities in The Manchester Metropolitan University and The Dermatology Centre, University of Manchester, Salford Royal NHS Foundation Trust. Project aims and objectives Aim: To predict the rate of hair loss or recovery in people with alopecia using computer vision and Artificial Intelligence (AI) algorithms. Objectives: Automate the hair segmentation process and predict the hair line. Design a classification algorithm to quantify the hair loss severity. Develop a mobile phone app as point of care for people with alopecia. Evaluate novel AI assessments using multimodal imaging and vision language models. Validate performance of the AI technology in clinical settings. Funding Both Home and International students can apply. Home tuition fees will be covered for the duration of the three years award, which is £5,006 for the year 2025/26. Eligible international students will need to make up the difference in tuition fee funding (Band 2 for the year 2025/26). The student will receive a standard stipend payment for the duration of the award. These payments are set at a level determined by the UKRI, currently £20,780 for the academic year 2025/26. Specific requirements of the candidate The candidate must be highly motivated and enthusiastic about advancing AI in a medical imaging context. Qualifications A high-grade undergraduate degree (first class or upper second) in Computer Science or MSc in related field. Skills Knowledge of programming, Computer Vision, Machine Learning, or related discipline. Experience with Python and relevant AI/ML libraries (e.g., PyTorch, TensorFlow). Experience with mobile application development. Demonstrated knowledge of multimodal data processing (e.g., vision, language, 3D). How to apply Interested applicants should contact Dr Adrian Davison for an informal discussion if they require further information or clarification. To apply you will need to complete the online application form for a full time PhD in Computing & Digital Technology Please complete the Doctoral Project Applicant Form , and include your CV and a covering letter to demonstrate how your skills and experience map to the aims and objectives of the project, the area of research and why you see this area as being of importance and interest. Please upload these documents in the supporting documents section of the University’s Admissions Portal. Applications closing date: 20 February 2026 Expected start date: October 2026 Please quote the reference: SciEng-AD-2026-27-Medical Imaging Dermatology

Disease estimates of years lost to disability, where alopecia areata (AA) was ranked higher than both psoriasis and melanoma in disease impact. Before significant diffuse hair thinning becomes clinically appreciable, over 50% scalp hairs need to be lost. Therefore, the process may have been occurring for many months or years before someone presents to their doctor. Current treatments are reasonable at stabilising hair loss but not very good at reversing it. Therefore, being able to identify the problem and treat early in the course of the disease would result in better outcomes for the patient. We are looking for candidates to research computer vision and artificial intelligence techniques to create an objective method of measuring areas of hair loss/recovery. This will tackle the problems of current technology, where it can be time-consuming, expensive, or require shaving of a patient’s hair. The successful candidate will benefit from training facilities in The Manchester Metropolitan University and The Dermatology Centre, University of Manchester, Salford Royal NHS Foundation Trust. Project aims and objectives Aim: To predict the rate of hair loss or recovery in people with alopecia using computer vision and Artificial Intelligence (AI) algorithms. Objectives: Automate the hair segmentation process and predict the hair line. Design a classification algorithm to quantify the hair loss severity. Develop a mobile phone app as point of care for people with alopecia. Evaluate novel AI assessments using multimodal imaging and vision language models. Validate performance of the AI technology in clinical settings. Funding Both Home and International students can apply. Home tuition fees will be covered for the duration of the three years award, which is £5,006 for the year 2025/26. Eligible international students will need to make up the difference in tuition fee funding (Band 2 for the year 2025/26). The student will receive a standard stipend payment for the duration of the award. These payments are set at a level determined by the UKRI, currently £20,780 for the academic year 2025/26. Specific requirements of the candidate The candidate must be highly motivated and enthusiastic about advancing AI in a medical imaging context. Qualifications A high-grade undergraduate degree (first class or upper second) in Computer Science or MSc in related field. Skills Knowledge of programming, Computer Vision, Machine Learning, or related discipline. Experience with Python and relevant AI/ML libraries (e.g., PyTorch, TensorFlow). Experience with mobile application development. Demonstrated knowledge of multimodal data processing (e.g., vision, language, 3D). How to apply Interested applicants should contact Dr Adrian Davison for an informal discussion if they require further information or clarification. To apply you will need to complete the online application form for a full time PhD in Computing & Digital Technology Please complete the Doctoral Project Applicant Form , and include your CV and a covering letter to demonstrate how your skills and experience map to the aims and objectives of the project, the area of research and why you see this area as being of importance and interest. Please upload these documents in the supporting documents section of the University’s Admissions Portal. Applications closing date: 20 February 2026 Expected start date: October 2026 Please quote the reference: SciEng-AD-2026-27-Medical Imaging Dermatology