Computational Biologist - Regenerative Genomics & scRNA-seq

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

Job summary

Tufts University’s Allen Discovery Center seeks an experienced computational biologist to lead design, analysis, and interpretation of high-dimensional datasets in limb regeneration studies using mouse and Xenopus models.

You will implement RNA-seq workflows, integrate cross-species data, and communicate findings via publications and presentations while collaborating with wet-lab researchers. Strong programming and statistical skills required.

Qualifications

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

Responsibilities

• Genomic and single-cell data analysis; develop reproducible pipelines for scRNA-seq and bulk RNA-seq.
• Collaborate with scientists to define hypotheses, sampling plans, and statistical approaches.
• Develop scripts and workflows; maintain version-controlled, documented code.
• Integrate molecular data with imaging and phenotypic outcomes; generate publications-ready figures.
• Assist with project meetings and translate analyses into follow-up experiments.
• Provide guidance on wet-lab workflows and experimental design.

Skills

scRNA-seq analysis

Python

R

Statistics

Experimental design

Reproducible analysis

Data visualization

Independent work

Education

Ph.D. with 1+ year experience

Master's with 5+ years experience

Tools

Python

R

Workflow managers

High-performance computing

Cloud computing

Job description

Tufts University’s Allen Discovery Center seeks an experienced computational biologist to lead design, analysis, and interpretation of high-dimensional datasets in limb regeneration studies using mouse and Xenopus models.

You will implement RNA-seq workflows, integrate cross-species data, and communicate findings via publications and presentations while collaborating with wet-lab researchers. Strong programming and statistical skills required.