Postdoctoral Fellow: Computational RNA Biology for Melanoma

MFS Investments; Boston (MA); On-site; USD 72,000 - 76,000; Full time

Job summary

Dana-Farber Cancer Institute in Boston invites applications for a Postdoctoral Fellow in Computational RNA Biology and Cancer. The Insco Laboratory studies how aberrant RNA processing and quality control contribute to cancer, with a focus on melanoma, using zebrafish models and human cell lines.

The successful candidate will analyze cutting-edge transcriptomic datasets, develop new computational tools, manage data and workflows, and contribute to fellowship applications and publications.

Qualifications

• PhD or equivalent doctoral degree in biomedical, computational, or related sciences.
• Experience analyzing complex transcriptomic or functional genomics datasets.
• Proficiency in Python and R with HPC experience required.
• RNA biology and cancer biology background is desirable; computational modeling a plus.

Responsibilities

• Analyze transcriptomic and functional genomics datasets (short-read RNA-seq, direct RNA sequencing, PAR-CLIP, DMS-MaP, Ribo-seq).
• Develop new computational tools to study the dark transcriptome in cancer.
• Develop reproducible workflows, document and share code, and deposit data in public repositories.
• Help manage lab data, computational infrastructure, and bioinformatics resources.
• Prepare fellowship applications, contribute to grant proposals, and lead research projects.
• Mentor trainees in computational biology and maintain organized research records.
• Interpret and discuss findings in lab meetings and present at conferences.

Skills

RNA biology

Cancer biology

Data analysis

Scientific writing

Education

PhD or equivalent doctoral degree

Tools

Python

R

High-Performance Computing

Job description

Dana-Farber Cancer Institute in Boston invites applications for a Postdoctoral Fellow in Computational RNA Biology and Cancer. The Insco Laboratory studies how aberrant RNA processing and quality control contribute to cancer, with a focus on melanoma, using zebrafish models and human cell lines.

The successful candidate will analyze cutting-edge transcriptomic datasets, develop new computational tools, manage data and workflows, and contribute to fellowship applications and publications.