Senior Computational Biologist, Regenerative Genomics

Tufts University; Medford (MA); On-site; USD 74,000 - 111,000; Full time

Job summary

Tufts University’s Allen Discovery Center seeks a computational biologist to lead high‑dimensional data analyses from limb regeneration studies in mouse and Xenopus. The role emphasizes single-cell and bulk RNA-seq, integrating data across experiments to drive biological insights.

The successful candidate will collaborate with wet-lab scientists, implement analytical workflows, and contribute to manuscripts and grants. Strong coding and visualization skills are essential.

Qualifications

• Ph.D. with at least 1 year of relevant experience or a Master's with at least 5 years in computational biology or related fields.
• Demonstrated experience analyzing single-cell RNA-sequencing data.
• Advanced proficiency in R and/or Python, with strong statistics and experimental design.
• Ability to work independently while collaborating with experimental scientists.
• Evidence of contributions to peer-reviewed publications.

Responsibilities

• Lead design, analysis, and interpretation of high-dimensional datasets, focusing on single-cell and bulk RNA-seq.
• Develop reproducible pipelines for scRNA-seq and related datasets, including QC and differential expression.
• Collaborate with experimentalists to ensure rigorous study design and appropriate power calculations.
• Adapt tools and create new methods; maintain code in Python and/or R with version control.
• Integrate molecular data with imaging and phenotypic outcomes; create publication-quality figures.

Skills

Single-cell data analysis

Genomics

Biostatistics

R

Python

Data visualization

Experimental design

Education

Ph.D. in computational biology or related field

Master's degree + 5+ years experience

Tools

Python

R

Git

Job description

Tufts University’s Allen Discovery Center seeks a computational biologist to lead high‑dimensional data analyses from limb regeneration studies in mouse and Xenopus. The role emphasizes single-cell and bulk RNA-seq, integrating data across experiments to drive biological insights.

The successful candidate will collaborate with wet-lab scientists, implement analytical workflows, and contribute to manuscripts and grants. Strong coding and visualization skills are essential.