Research Associate/Bioinformatician

1000scholars; Basel; Presencial; CHF 95 000 - 150 000; Tempo integral

Resumo da oferta

IOB Basel seeks a Research Associate/Bioinformatician to lead quantitative analysis of retinal imaging datasets and to develop longitudinal models of disease progression for patient stratification and outcome prediction.

You will work at the intersection of computational biology, medical image analysis, and translational ophthalmology, collaborating with clinicians to translate computational findings into clinical trial design and endpoint development.

Qualificações

• MSc or PhD in computational biology, bioinformatics, CS, biomedical engineering, physics, statistics, or related quantitative field.
• Strong Python programming skills and reproducible workflows.
• Experience with complex high-dimensional biomedical or imaging datasets.
• Ability to explain methods to both computational and clinical collaborators.
• Evidence of independent quantitative research (thesis, publication, software, or analytics).

Responsabilidades

• Analyze large-scale retinal imaging datasets (OCT, fundus) using deep-learning imaging features.
• Develop and validate statistical and ML models of disease progression.
• Build patient stratification and phenotyping pipelines.
• Collaborate with clinicians to translate findings into trial design and endpoints.
• Contribute to publications and present findings at conferences.

Conhecimentos

Python

Data analysis workflows

High-dimensional data handling

Communication to clinicians

Independent quantitative research

Formação académica

MSc or PhD in computational biology / bioinformatics / related field

Ferramentas

Python (scikit-learn, PyTorch)

Descrição da oferta de emprego

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.