Computational Evolutionary Biology Co-op/Intern, Master's/PhD Students Preferred

We are seeking a highly motivated individual with expert knowledge of computational evolutionary biology and extensive quantitative skills to join our Adeno Associated Viral vectors (AAV) Technology Laboratory at Sanofi’s Genomic Medicine Unit (GMU) located in Waltham, Massachusetts, USA, as an Intern/Co-op.

As an intern/co-op you will contribute to evolutionary biology-guided identification and engineering of next-gen AAV/LNP (lipid nano particle) gene therapy products for controllable tissue tropism.

Precise targeting of organ of interest is necessary to limit side effects of gene therapies and, thus has a potential to improve patients’ lives. You will develop evolutionary biology algorithms, run simulations to explore the sequence-to-function landscape of AAV/LNP and identify methods to efficiently generate top candidate sequences with custom features. In addition, you will explore combining external biological datasets with proprietary High Throughput Screening data to build predictive models of biodistribution and to derive principles guiding biodistribution of gene therapies to organs.

The selected individual will develop cutting-edge evolutionary biology frameworks, integrate various biological datasets, customize various algorithms, and generate predictions for experimental validation. The successful candidate will work in a dynamic and multi-disciplinary environment on next-gen projects with potential to impact human health.

This is a paid, on-site, full time position. You will be a co-author on any resulting patents or publications from this work.

Key Responsibilities:

· Develop evolutionary biology algorithms, run simulations, integrate different biological datasets

· Perform data analyses and interpret experimental results

· Present research findings and progress at project team meetings

Required Qualifications:

· PhD level candidates or exceptional Master’s candidates

· Background in evolutionary biology, phylogenetic analysis, genomics

· Experience building models, running evolutionary simulations, developing algorithms

· Programming in python, R

· Familiarity with HPC and/or AWS

· Experience with data analysis and public repositories of biological data

· Good communication skills

· Self-motivated and willing to learn

Preferred Qualifications:

· Background in structural biology is a plus