The DAISy-PCOS team have a new PhD studentship opportunity in Machine Learning / Computer Science available, supervised by DAISy-PCOS Principle Investigator Professor Wiebke Arlt from the Institute of Metabolism & Systems Research and Professor Peter Tino from the School of Computer Science
Project title: Integrated machine learning approaches to dissect the link between androgen excess and metabolic disease
This multi-disciplinary PhD project will focus on the development of innovative machine learning approaches for the investigation of steroid metabolome, global non-targeted metabolome and clinical phenotype data obtained in a large cohort of women with PCOS.
Application deadline: 5 March 2021
Applicants should have a strong background in a numerate discipline (Computer Science, Mathematics, Physics or a related field), with strong interest in computational modelling and machine learning, including algorithm development and applications. Good programming skills are necessary, experience with applications in life sciences and biomedical data analysis is desirable.
Applicants should be enthusiastic, self-motivated, and excellent team players. Due to the collaborative and inter-disciplinary nature of the project, outstanding communication skills and the ability and enjoyment of analytical, translational and creative thinking are required. Applicants should hold or realistically expect to obtain a Masters of Science (or equivalent) degree with excellent marks.