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A leading research university in Singapore is seeking a Research Fellow for AI in medicine research, specifically focusing on developing models for medical image analysis. The ideal candidate will hold a PhD in Artificial Intelligence or a related field, with proficiency in deep learning frameworks. This role includes mentoring students, ensuring compliance with ethical standards, and collaborating with interdisciplinary teams to enhance patient care through advanced AI technologies.
The Lee Kong Chian School of Medicine (LKCMedicine) trains doctors who put patients at the centre of their exemplary care. The School, which offers both undergraduate and graduate programmes, is named after local philanthropist Tan Sri Dato Lee Kong Chian. Established in 2010 by Nanyang Technological University, Singapore, in partnership with Imperial College London, LKCMedicine aims to be a model for innovative medical education and a centre for transformative research. The School’s primary clinical partner is the National Healthcare Group, a leader in public healthcare recognised for the quality of its medical expertise, facilities and teaching. The School is transitioning to an NTU medical school ahead of the 2028 successful conclusion of the NTU-Imperial partnership to set up a Joint Medical School. In August 2024, we welcomed our first intake of the NTU MBBS programme, that has been recently enhanced to include themes like precision medicine and Artificial Intelligence (AI) in healthcare, with an expanded scope in the medical humanities. Graduates from the five-year undergraduate medical degree programme will have a strong understanding of the scientific basis of medicine, with an emphasis on technology, data science and the humanities.
We are looking for a Research Fellow to conduct AI for medicine research. The role will focus on developing foundation models to medical image analysis. Foundation models offer a scalable and adaptable solution for medical image analysis by learning generalizable representations from large datasets, enabling effective application across different imaging modalities and tasks. Their ability to perform well with limited labeled data and adapt quickly to new clinical scenarios makes them a powerful tool for advancing AI-driven diagnostics and improving patient care. The position aligns with the School's mission to advance research on AI in medicine and improve patient outcomes through cutting-edge AI technologies.
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