PhD Position in Computational Biology: Digital Twins & Disease Modeling for Precision Osteoarthritis Treatments

Osteoarthritis (OA) is a complex disease in which multiple tissues in the joint are affected. Recently, multiple OA risk genes were identified based on small genetic variations (SNPs) in the regulatory sequences of these genes. These variations can lead to unfavorable expression of these genes contributing to disease progression. OA patients have different unique combinations of SNPs that lead to OA onset and progression. In this project we will uncover the integrated effects of deviant OA risk gene expression in cartilage with the aim to develop effective disease-modifying therapies. As a PhD student, you will play a central role in this process by building an in silico disease map that models these effects and enables more precise treatments.

You will be part of the multidisciplinary research group of Dr. Janine Post, specializing in quantitative biology and computational approaches to understanding cell fate. This position is ideal for those excited about bridging biology with data science to develop innovative digital twin models for precision medicine.

As a PhD student, you will:

• Build computational models that integrate molecular and genetic data to study possible OA treatments.
• Develop bioinformatics pipelines, network-based approaches, or dynamic models to predict gene regulatory interactions.
• Work with digital twin technology, simulating patient-specific disease progression and treatment responses.
• Collaborate in an interdisciplinary research environment, bridging molecular biology, genetics, and computational science.

We welcome applicants with backgrounds in:

• Biomedical engineering, bioinformatics, systems biology, genetics, or a related field.
• Experience with computational modeling, data analysis, and/ or bioinformatics tools (e.g., Python, R, MATLAB).
• Strong communication skills with experts in the fields of molecular biology, genetics and mathematical modeling
• A strong motivation to apply computational approaches to biological questions.
• A passion for translational research