The Unit on Clinical Investigation of Retinal Degenerative Diseases, Division of Epidemiology and Clinical Applications (DECA), National Eye Institute (NEI), National Institutes of Health (NIH) in Bethesda, Maryland, USA, is seeking a skilled and motivated researcher with expertise in image processing, algorithm development, and deep learning.
The section conducts prospective clinical studies in normal and abnormal functioning of the retina, particularly those of retinal degenerations – including complex retina diseases such as age-related macular degeneration (AMD), drug toxicities, and monogenic retina disease. The major aims of the section include an understanding of the pathophysiology of retinal disease as assessed with functional testing and multi-modal imaging to develop and assess appropriate outcome measurements in clinical trials. Acquired images are from the following modalities: optical coherence tomography (OCT) and angiography, fundus color photographs, fundus autofluorescence (FAF), and adaptive optics (AO). Accumulated datasets of longitudinal, multimodal images collected prospectively will be used to answer both hypothesis-driven and hypothesis-generating research to add to the understanding of the anatomic changes occurring in retinal disease. Researchers with experience in processing these types of data/images in prior work will be given priority.
It is desired that the scientist will develop automatic segmentation algorithms to detect/segment/quantify ocular anatomical/pathological structures seen on ophthalmic imaging systems (such as OCT, FAF, AO). The prospective scientist will also develop automated software, using deep learning and other advanced machine learning technologies, to objectively detect and evaluate the biomarkers for onset and progression of the retinal diseases studied. Computational analyses will involve combinatorial assessment of disparate data types (i.e. patient demographics, risk factors, genetic information, different imaging modalities, etc.) into comprehensive classification and regression models. Familiarity with development and optimization for CUDA and/or OpenCL workflows and applications is desirable.
The scientist will participate in experimental design, data collection, data analysis and manuscript preparation; assist intramural staff, including students and postdoctoral fellows; stay current with scientific literature; stay informed about new approaches and technologies, including participation in conferences, workshops, and/or formal classroom instruction; independently locate and utilize scientific resources; and co-author peer reviewed publications.
The ideal candidate will:
- Hold a PhD degree or equivalent, with significant postdoctoral experience.
- Have a proven record of publications that provide evidence of their expertise.
- Have significant published work using computer programming in one or all the following languages: Python, MATLAB, C++, R, and/or Java.
- Have experience in image analysis (registration, segmentation, and/or tracking) in medical images, with priority given to those with the ophthalmological modalities listed above.
- Experience with large data sets (>1 TB) and deep learning will be given priority.
- Have a strong work ethic, and the ability to design projects within our research areas and available datasets.
- Possess excellent oral and written communication skills in English, and recordkeeping skills.
Salary will be commensurate with education and experience. A full benefits package, including health and retirement benefits, is available. U.S. citizens, U.S. permanent residents, and non-U.S. citizens are eligible to apply.
Interested candidates may contact Dr. Catherine Cukras via email with their CV, firstname.lastname@example.org, for additional information about the position. To apply, candidates must submit a curriculum vitae, statement of research interests, and contact information for three referees who will be asked to provide letters of reference.
Visit https://nei.nih.gov/intramural for more information about NEI.
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