Computational Biologist

Tufts University•Medford, MA

About The Position

The Allen Discovery Center is dedicated to advancing basic biology and biomedicine through integrated molecular, genetic, biophysical, computational, and engineering approaches to understand and control biological form and function. The Morphogenesis and Regeneration Group investigates mechanisms of limb regeneration and regenerative biology using mouse and Xenopus models, with the goal of identifying principles and interventions that improve tissue repair, patterning, and functional regeneration. We are seeking an experienced computational biologist to support the Morphogenesis and Regeneration Group's studies of limb regeneration and regenerative biology in mouse and Xenopus models. Reporting to PI Michael Levin and working collaboratively alongside other senior scientists and team members, this individual will lead the design, analysis, and interpretation of high-dimensional datasets, with particular emphasis on single-cell and bulk RNA sequencing. The position will partner closely with experimental researchers to ensure that studies are statistically rigorous, appropriately powered, and structured to answer defined biological questions. The successful candidate will implement established analytical workflows, develop new computational approaches when existing tools are insufficient, integrate data across experiments and species, and communicate findings through clear visualizations, reports, presentations, and manuscripts. Familiarity with wet-laboratory molecular biology workflows is important for effective experimental design and interpretation, such as RNA purification, library prep, single cell isolation, in-situ hybridization, and antibody staining. In this role, additional responsibilities include participating in teaching, research, and mentoring including providing research guidance to graduate students or postdoctoral scholars.

Requirements

Master's Degree Degree in computational biology, bioinformatics, biostatistics, genomics, systems biology or related field
One year experience with PhD
Five years' experience with Master's degree
Demonstrated experience analyzing single-cell RNA-sequencing data.
Advanced proficiency in R and/or Python.
Strong grounding in statistics, experimental design, reproducible analysis, and scientific data visualization.
Ability to work independently while collaborating closely with experimental scientists.
Evidence of contributions to peer-reviewed publications is required.

Nice To Haves

PhD Degree in computational biology, bioinformatics, biostatistics, genomics, systems biology or related field
Experience with regenerative biology, developmental biology, limb biology, mouse or Xenopus datasets, spatial transcriptomics (highly desired), multi-omics integration, image analysis, workflow managers, high-performance computing, or cloud computing.
Wet-laboratory experience with RNA purification, library preparation, single-cell isolation, in situ hybridization, or immunostaining is strongly preferred.

Responsibilities

Genomic and single-cell data analysis
Develop and execute reproducible pipelines for single-cell RNA-seq, bulk RNA-seq, and related genomic datasets.
Perform quality control, normalization, clustering, differential expression, trajectory, pathway, cell-cell communication, and cross-species analyses as appropriate.
Experimental design and biostatistics
Collaborate with scientists before data collection to define hypotheses, sampling plans, replicate structure, covariates, and statistical approaches.
Select and document appropriate methods for significance testing, multiple-comparison correction, batch effects, and large or nested datasets.
Computational method and tool development
Adapt established tools and develop new scripts, workflows, or analytical methods when existing approaches do not adequately address the biological question.
Maintain version-controlled, documented, and reusable code in Python and/or R.
Data integration, interpretation, and visualization
Integrate molecular data with imaging, phenotypic, treatment, developmental-stage, and regeneration outcomes.
Generate publication-quality figures and concise scientific summaries for internal decision-making.
Scientific collaboration and wet-lab interface
Advise on sample collection, RNA purification, library preparation, single-cell isolation, in situ hybridization, and antibody-staining workflows.
Participate in project meetings and help translate analytical findings into follow-up experiments.
Documentation and dissemination
Maintain organized analytical records and data provenance.
Contribute to manuscripts, grant reports, presentations, and shared protocols.