Postdoctoral Fellow – Machine Learning for Predicting Glaucoma Progression – National University of Singapore

Job description: We are looking for a bright, dynamic, and highly motivated individual to perform research in artificial intelligence with applications to ophthalmology. This is a project funded by the Biomedical Institute for Global Health Research and Technology (BIGHEART: http://bigheart.nus.edu.sg) and in collaborations in the NUS Departments of Biomedical Engineering and Statistics, and the Singapore Eye Research Institute (Top 5 worldwide).
The proposed study aims to use optical coherence tomography imaging and artificial intelligence (deep learning) to predict vision loss progression from glaucoma – a blinding ocular disorder for which mechanisms are far from being understood. Predicting glaucoma progression is subjective, heavily dependent on a clinician’s experience/expertise, and requires multiple clinical tests. Such tests often need to be repeated at multiple patients’ visits to overcome their inherent subjectivity. Recently, the World Glaucoma Association stated: “No specific test can be regarded as the perfect reference standard for detection of glaucomatous […] progression”. In other words, clinicians cannot identify which patients are most likely to lose vision, and how quickly. This means that over- and under-treatments are inevitable. Since structural changes of the eye almost always precede vision loss in glaucoma, we aim to exploit the rich information available in 3D images of the eye to predict glaucoma progression. We will achieve this by combining key image enhancement and artificial intelligence technologies. We believe our solution will could to better personalized treatment for the benefit of glaucoma patients.
For this project, the successful candidate will develop 3D deep learning algorithms to predict structural and functional changes of the eye. We will use a longitudinal data set of 3D optical coherence tomography images of the eye for training our algorithms. Due to data scarcity and heterogeneity of data acquisition modalities, Bayesian regularization techniques, robust uncertainty quantification and representation learning are likely to be crucial components of the methodologies developed in this project.
The candidate will also be expected to manage and lead a team of PhD students and Research Associates.
Qualification: A minimum of 2-years experience with deep learning algorithms, and several deep learning publications are required. Excellent communication and English-writing skills are also required. No background in ophthalmology is required, however, the candidate will be expected to become extremely knowledgeable in the field of clinical glaucoma in order to interact with clinicians. Candidates with PhDs in Computer Science, Electrical Engineering, Biomedical Engineering, Mathematics, Statistics or other related disciplines are encouraged to apply.
Starting Date: Immediately
Duration: An initial contract of 12 month will be provided. Upon performance, we have the funds to extend this contract for a total duration of 2-3 years.
Salary Range: S$70 to S$90K per year (dependent on qualifications)
To apply, please email a detailed CV and the names of two references to:
Dr. Michael JA Girard
Ophthalmic Engineering & Innovation Laboratory
Department of Biomedical Engineering
National University of Singapore
Email: mgirard-AT-nus.edu.sg
Homepage: http://www.bioeng.nus.edu.sg/oeil/