Research Associate/Bioinformatician
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Research Associate/Bioinformatician
IOB - Institute of Molecular and Clinical Ophthalmology Basel
Basel, Switzerland
We are looking for a Research Associate/Bioinformatician to lead the quantitative analysis of retinal imaging datasets. You will work with high-dimensional representations learned from retinal images, develop longitudinal models of disease progression, and build data-driven approaches for patient stratification and clinically relevant outcome prediction. The position sits at the intersection of computational biology, medical image analysis, and translational ophthalmology.
Your responsibilities
- 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.
Requirements
Minimum qualifications
- 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.
Preferred qualifications
- 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.
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