Metabolic Genomics Postdoc – Variant-to-Function Research

Broad Institute; Cambridge (MA); On-site; USD 60,000 - 78,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) retirement plan

Paid time off

Job summary

The Broad Institute invites applications for a postdoctoral associate in the Variant-to-Function (V2F) team, conducting cutting-edge studies on genetic variants linked to metabolic diseases. You will leverage CRISPR screens, single-cell sequencing, and high-content imaging to dissect regulatory networks in adipocytes, hepatocytes and myocytes, collaborating across experimental and computational scientists.

Ideal candidates hold a PhD with strong data interpretation skills and a track record in

Qualifications

• PhD in molecular biology, cellular biology, or related discipline.
• 0+ years of post‑graduate experience.
• Independent and collaborative researcher with ability to work in a fast-paced, highly collaborative environment.
• Strong interpersonal, verbal and written communication skills.

Responsibilities

• Develop and implement functional genomics techniques and high throughput screening strategies to assess variant and gene effects.
• Plan and execute projects independently while working with a multidisciplinary team.
• Document, compile, analyze, and interpret experimental data.

Skills

Independent researcher

Collaborative

Strong communication

Organizational skills

Time management

Analytical thinking

Education

PhD in molecular biology or related discipline

Tools

CRISPR genome editing

Single-cell RNA sequencing

ATAC-seq

High-content imaging

Pooled/genome-scale screens

Job description

The Broad Institute invites applications for a postdoctoral associate in the Variant-to-Function (V2F) team, conducting cutting-edge studies on genetic variants linked to metabolic diseases. You will leverage CRISPR screens, single-cell sequencing, and high-content imaging to dissect regulatory networks in adipocytes, hepatocytes and myocytes, collaborating across experimental and computational scientists.

Ideal candidates hold a PhD with strong data interpretation skills and a track record in