Research Associate / Bioinformatician

Analyze large-scale retinal imaging datasets (e.g. OCT, fundus photography) using deep-learning-derived imaging features Develop and validate statistical and machine learning models of disease progression Build patient stratification and phenotyping pipelines Collaborate with clinicians to translate computational findings into clinical trial design, patient selection criteria, and endpoint development Contribute to the group's publication output and present findings at international conferences

The Institute of Molecular and Clinical Ophthalmology Basel (IOB) combines basic and clinical research to advance the understanding of vision and its diseases and to develop new therapies for vision loss. The Retinal and Macular Degeneration Clinical Research Group conducts translational and clinical research to improve how retinal diseases are characterized, monitored, and treated. The group studies retinal structure and function across diseases and disease stages, follows how these measures change over time, and evaluates responses to therapy. Its work spans multimodal imaging, functional testing, computational analysis, biomarker and endpoint development, patient stratification, natural-history studies, and interventional clinical trials. Its work connects early-stage therapeutic development with large-scale clinical collaboration across more than 30 international partner sites in Europe, North America, and Asia, helping to translate laboratory discoveries into clinical applications. The position sits at the intersection of computational biology, medical image analysis, and translational ophthalmology.

• MSc or PhD in computational biology, bioinformatics, computer science, biomedical engineering, physics, statistics, or a related quantitative field

• Strong Python programming skills and experience developing reproducible data-analysis workflows

• Experience working with complex, high-dimensional biomedical or imaging datasets, including data quality control and validation

• Ability to explain technical methods and results clearly to both computational and clinical collaborators

• Evidence of independent quantitative research, such as a thesis, publication, preprint, software project, or substantial analytical contribution

• Experience with medical image analysis, retinal imaging, or ophthalmic data

• Experience with survival analysis frameworks, mixed-effects modeling, or Bayesian approaches to disease progression

• Experience with representation learning and generative or temporal modelling in learned representation spaces, for example using diffusion models, latent-variable models, or neural state-space models

• Experience with statistical modelling or machine learning applied to longitudinal, repeated-measures, or time-to-event data

• Experience with high-dimensional data analysis (e.g. embeddings, single-cell, or omics data), including dimensionality reduction, clustering, and phenotyping