Staff Scientist

prospective scientists will join a multidisciplinary research group focused on clinical research in retinal ophthalmic disease by way of large, longitudinal datasets of multimodal images and associated functional outcomes. The Staff Scientist will develop, validate, and deploy advanced image analysis and machine learning methods to address questions in retinal disease progression, structure-function relationships, and clinical trial/natural history datasets, with a particular emphasis on diseases such as age-related macular degeneration (AMD) and diabetic retinopathy.

Key responsibilities include:

• Designing, implementing, and evaluating state-of-the-art algorithms for retinal image analysis (e.g., segmentation, detection, quantification) across modalities such as OCT and other clinical imaging types
• Developing machine learning approaches for modeling retinal disease progression, including spatially resolved and longitudinal prediction tasks.
• Integrating imaging-derived biomarkers with clinical and functional measures to study structure–function relationships and develop predictive models of visual function.
• Leading rigorous validation, reproducibility, and documentation of analytic pipelines; contribute to publications, presentations, and open, maintainable scientific software practices.
• Collaborating closely with clinicians, statisticians, and scientists across NEI/NIH.
• Contributing to technical mentorship and training of trainees in computational methods.

Required qualifications:

• A Ph.D. (or equivalent degree) in Computer Science, Biomedical Engineering, Computational Science, or a closely related quantitative discipline, with demonstrated postdoctoral, or equivalent, research experience.
• Demonstrated expertise in image analysis and machine learning/deep learning for biomedical imaging or related high-dimensional imaging applications.
• Strong programming skills in Python (and/or comparable scientific programming languages), including experience with modern deep learning and computer vision frameworks.
• Evidence of scientific productivity (peer-reviewed publications, conference presentations, and/or deployed analytic tools).
• The Ability to work independently and collaboratively in a multidisciplinary environment, with strong communication skills and a commitment to reproducible research practices. Preferred qualifications (one or more of the following):
• Experience with retinal imaging and/or ophthalmic image analysis (e.g., OCT-based retinal layer/lesion segmentation; multimodal retinal imaging).
• Familiarity with retinal diseases and clinical research contexts (e.g., AMD, geographic atrophy, diabetic retinopathy), including longitudinal imaging datasets and clinical endpoints.
• Experience in domain adaptation, generalization across devices/sites, and robust validation strategies for clinical imaging ML.

Selection for this position will be based solely on merit, with no discrimination for non-merit reasons such as race, color, religion, gender, sexual orientation, national origin, political affiliation, marital status, disability, age, or membership or non-membership in an employee organization. The NIH encourages the application and nomination of qualified women, minorities, and individuals with disabilities.