Research Associate in Machine Learning

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Website Cambridge_Uni Cambridge University

Closing date: 3rd January

Research Associate in Machine Learning for Brain and Mental Health (Fixed Term)

University of Cambridge – Department of Applied Mathematics and Theoretical Physics

Applications are invited for a Research Associate position in Machine Learning for Brain and Mental Health to work with Prof Carola-Bibiane Schönlieb in the Cambridge Image Analysis Group, Department of Applied Mathematics and Theoretical Physics, and Prof Zoe Kourtzi at the Adaptive Brain Lab ( University of Cambridge.

The position will focus on the development and implementation of state-of-the-art machine learning and image analysis techniques for the early diagnosis of mental health disorders (e.g. dementia, mood-related disorders). The research aims to develop biologically-inspired artificial systems for precision brain and mental health by bringing together expertise in machine learning, data science, neuroscience, and clinical practice.

Duties include developing and conducting individual and collaborative research objectives, proposals and projects. The role holder will be expected to plan and manage their own research and administration, with guidance if required, and to assist in the preparation of proposals and applications to external bodies. You must be able to communicate material of a technical nature and be able to build internal and external contacts. You may be asked to assist in the supervision of student projects, the development of student research skills, provide instruction or plan/deliver seminars relating to the research area.

The successful candidate will have or about to be awarded a PhD in a relevant area (e.g. Mathematics, Computer Science, Engineering, Biostatistics, Neuroscience, Medicine), together with a strong academic track record. Programming skills are highly desirable and experience with machine learning, data science, medical image analysis, biostatistics, or clinical neuroscience/neurology are highly beneficial. Above all, they will demonstrate enthusiasm to contribute new knowledge, openness to learn new approaches and willingness to contribute to a multidisciplinary team across academia and industry.

Limit of tenure: 1 year (fixed term) in the first instance, with a potential extension up to 3 years.

Informal inquiries can be made by contacting or Professor Zoe Kourtzi

Please quote reference LE28996 on your application and in any correspondence about this vacancy.

To apply for this job please visit

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