We are looking for a highly skilled bioinformatics postdoctoral Research Fellow who specializes in researching, designing, developing, deploying, and maintaining scalable bioinformatics pipelines on cloud-based infrastructure. The Research Fellow will be responsible for the code base supporting the large-scale genomic processing and analysis pipelines at the SMaHT Data Analysis Center that manages multi-omic data (e.g., Illumina/PacBio/ONT Whole Genome Sequencing (WGS), RNA-Seq). The ideal candidate will have a deep understanding of next-generation sequencing (NGS) data analysis, workflow automation, and cloud computing. This role will support research and production environments where reproducibility, scalability, and performance are critical.
The successful candidates will join a group of supportive, mission-driven, and positively busy computational biologists and have an opportunity to collaborate with world-class biologists in the Harvard Medical School area.
Several positions are available in the Park Lab (compbio.hms.harvard.edu). The aim of the laboratory is to develop and apply innovative computational methods for genome sequencing data to enhance our understanding of disease processes, including cancer and brain-related diseases.
Basic Qualifications:
An ideal candidate will have a PhD in computational biology/bioinformatics/statistics/CS or another quantitative field, as well as superb programming (Python, shell scripting) and communication skills.
Additional Qualifications:
Extensive experience with analysis of highthroughput sequencing data and knowledge of bioinformatics tools for sequence alignment, variant calling, sequence data QC, etc.
Proficiency in Docker for creating a reproducible execution environment and Workflow Description Language for orchestrating complex tasks.
Strong understanding of AWS services (EC2, S3) or similar cloud platforms for compute and storage.
Version Control & CI/CD: Git, automated testing, deployment workflows.
Experience with Linux systems, HPC, and distributed computing environments.
Knowledge of optimizing pipelines for large-scale genomic projects.
Please send a CV to Elizabeth Chun (elizabeth_chun@hms.harvard.edu) or apply here: academicpositions.harvard.edu/postings/15918.