Primary Purpose: The Taghibiglou Research Group (Dr. Changiz Taghibiglou, https://neuroscience.usask.ca/people/faculty/dr.-changiz-taghibiglou.php; https://medicine.usask.ca/profiles/anatomy-physiology-pharmacology/changiz-taghibiglou.php ) at the University of Saskatchewan (USask) in collaboration with Clinical Colleagues: Dr. Andrew Kirk (Div. Neurology, U of S), Dr. Ravi Nrusimhadevara (Dept. Ophthalmology, U of S), Dr. Melody Wong (Dept. Ophthalmology, U of S),  (Dr. Kerry Bishop (Optometrist, private practice) and Dr. Francisco Cayabyab (Dept. Surgery, U of S) are looking to recruit a talented and motivated Neuroscience postdoctoral fellow. In addition to neuroscience research experience, having familiar with machine learning/AI/ big data processing will be an asset. A major part of this PDF responsibility is to explore innovative machine learning strategies for analyzing patients existing OCT and OCT-A images in Saskatchewan Optometry and Ophthalmology clinics as well as internationally available data sources such as UK Biobank and eventually come up with algorithm useable for the early detection of Alzheimer’s disease (AD) and Parkinson’s disease (PD).

Nature of Work: In this project, we will analyze existing optical coherence tomography (OCT) and optical coherence tomography angiography (OCT-A) in Saskatchewan (Saskatoon, Regina, and Prince Albert) eye doctor clinics and internationally available data sources in healthy aged individuals and patients diagnosed with AD and PD (sex and age matched) for alterations in optical and retinal layers thickness/density. Our goal is to use machine learning technology/AI as a tool to analyze these images and come up with an algorithm to be utilized for early detection of AD/PD when the patient has no visible sign and symptoms.

This project is team-based, collaborative, and interdisciplinary.

Accountabilities: The PDF will report directly to Dr. Taghibiglou. The selected candidate will be expected to play a leadership role in the project, providing training and guidance to junior undergraduate and graduate students.

Education: A PhD in Neuroscience, Computational Neuroscience, Machine Learning in image analysis, or a related field, with significant experience in OCT and OCT-A image analysis.

Experience: The selected candidate should have an established Neuroscience skillset particularly in Machine Learning and writing algorithm. Previous experience in brain image analysis is ideal but not required.

Skills: The candidate should have the skills and experience necessary to independently analyze OCT and OCT-A images of optical nerve and retinal layers, perform statistical analysis of collected data and write relevant algorithm. Good writing and presentation skills are ideal for scientific communication.

To Apply: Interested applicants must include the following in their application: cover letter, curriculum vitae, and a publication from their previous research work showcasing their skills relevant to this posting