Research Associate/bioinformatician
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- Institute of Molecular and Clinical Ophthalmology Basel /pp Basel, Switzerland /pp 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. /ph3 Your responsibilities /h3ulli Analyze large-scale retinal imaging datasets (e.g. OCT, fundus photography) using deep-learning-derived imaging features; /lili Develop and validate statistical and machine learning models of disease progression; /lili Build patient stratification and phenotyping pipelines; /lili Collaborate with clinicians to translate computational findings into clinical trial design, patient selection criteria, and endpoint development; /lili Contribute to the group's publication output and present findings at international conferences. /li /ulh3 Requirements /h3h3 Minimum qualifications /h3ulli MSc or Ph D in computational biology, bioinformatics, computer science, biomedical engineering, physics, statistics, or a related quantitative field; /lili Strong Python programming skills and experience developing reproducible data-analysis workflows; /lili Experience working with complex, high-dimensional biomedical or imaging datasets, including data quality control and validation; /lili Ability to explain technical methods and results clearly to both computational and clinical collaborators; /lili Evidence of independent quantitative research, such as a thesis, publication, preprint, software project, or substantial analytical contribution. /li /ulh3 Preferred qualifications /h3ulli Experience with medical image analysis, retinal imaging, or ophthalmic data; /lili Experience with survival analysis frameworks, mixed-effects modeling, or Bayesian approaches to disease progression; /lili 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; /lili Experience with statistical modelling or machine learning applied to longitudinal, repeated-measures, or time-to-event data; /lili Experience with high-dimensional data analysis (e.g. embeddings, single-cell, or omics data), including dimensionality reduction, clustering, and phenotyping. /li /ul
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