Graduate Teaching Associate – Computer Science
We offer an exciting Graduate Teaching Associate opportunity in Computer Science within the School of Computing and Mathematics, supporting graduates in relevant disciplines to develop the skills and experience to become the lecturers of the future. These are excellent development roles for graduates with an aspiration to enter an academic career. Employed two days a week to support teaching, the GTA will pursue doctoral research studies on the remaining three days, supported by a stipend.
Our School pursues impactful, applied research while providing an excellent education and experience for our students. Each GTA will develop the ability to fulfil both of these expectations. Research and teaching will be in Computer Science and related areas, with a PhD project entitled “Explainable Artificial Intelligence and Machine Learning for Diagnosis of Skin Conditions”. The project is in collaboration with the Clinical Bioinformatics team, Cardiff and Vale University Health Board (CVUHB) headed by Professor Colin Gibson and the Institute of Dermatology, University Hospital of Wales, Cardiff represented by Dr Richard Motley.
Each GTA position will be for a period of up to 5 years, during which we will support the appointees to develop their experience in teaching and assessment, and complete their doctoral studies. Each School will agree tailored teaching and research development plans with the GTA.
At the end of the period, a successful GTA will have completed their doctoral studies and gained the experience necessary to fulfil a Lecturer role.
This fixed-term position is based at the Pontypridd Campus of the University of South Wales. A GTA must also meet the progression requirements of a part-time PhD student.
Explainable Artificial Intelligence and Machine Learning for Diagnosis of Skin Conditions -
AI systems have been highly successful in many applications including face recognition, autonomous driving, image classification, or medical diagnosis, particularly when problems can be expressed as data classification or pattern recognition tasks. However, AI systems particularly deep learning methods often turn out to be “black boxes,” which create significant challenges in terms of interpreting a predictive result or verifying the accuracy of diagnosis. In medical diagnosis, for example, one typically seeks not just an answer or an output but also an explanation or structuring of evidence used to support such a prediction. This aim of the project is to address this problem and develop AI model to aid General Practitioners in skin monitoring and early melanoma detection that is also able to explain its decisions in both visual and textual modalities.
The proposed research will test the hypothesis that successful implementation of the machine learning algorithm can improve diagnostic confidence and ability of General Practitioners to manage patients within their own practice rather than relying on time consuming and costly referrals for face-to-face consultation with a dermatologist. The academic challenge involves developing a novel machine learning-based approach able to use the clinical evaluations of physicians to enhance their diagnostic capability. The goal of the algorithm is to generate explanations that are relevant both in terms of image description and diagnostic classification.
For further information and to apply for this post please visit: https://jobs.southwales.ac.uk/en/vacancies/6527/graduate_teaching_associate_computer_science/#job-advert