Research Associate / Bioinformatician

Your position

• 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

Your profile

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