Postdoctoral Research Fellow - Insco Lab

A postdoctoral fellow position is available in the Insco Laboratory in the Department of Medical Oncology at Dana-Farber Cancer Institute (DFCI). We are seeking an enthusiastic and highly motivated scientist to join our team!

The Insco Lab investigates how aberrant RNA processing and defects in nuclear RNA quality control contribute to cancer, with a particular focus on melanoma. We are especially interested in the dark transcriptome, including aberrant RNAs derived from repetitive genomic elements and mis-spliced transcripts. By combining zebrafish models, mechanistic studies in human melanoma cell lines, and analyses of patient-derived datasets, we seek to uncover clinically relevant RNA-driven oncogenic mechanisms and identify therapeutic vulnerabilities in high-risk melanoma.

Our lab fosters a culture of curiosity, rigor, collaboration, and teamwork. Each scientist leads an independent project while benefiting from the collective expertise and support of the group. As a physician-scientist-led laboratory, we are committed to discovering and targeting patient-relevant mechanisms, providing individualized mentorship, and helping each lab member achieve their professional goals both during their time in the lab and beyond.

This position will be jointly mentored by Dr. David Liu . Liu is a melanoma medical oncologist and translational scientist with a background in software engineering. His laboratory develops biologically informed predictive models using statistical and machine-learning approaches. The successful candidate will analyze cutting-edge transcriptomic datasets from model organisms and patient samples. These may include data generated using Oxford Nanopore direct RNA sequencing, PAR-CLIP, ribosome profiling (Ribo-seq), single-cell RNA sequencing, and DMS-MaP.

Projects may involve dark transcriptome annotation, peptide prediction, RNA modification analysis, and RNA structure analysis.

Located in Boston and the surrounding communities, Dana-Farber Cancer Institute is a leader in life changing breakthroughs in cancer research and patient care. We are united in our mission of conquering cancer, HIV/AIDS, and related diseases. We strive to create an inclusive, diverse, and equitable environment where we provide compassionate and comprehensive care to patients of all backgrounds, and design programs to promote public health particularly among high-risk and underserved populations.

We conduct groundbreaking research that advances treatment, we educate tomorrow's physician/researchers, and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.

• Apply existing computational tools to analyze transcriptomic and functional genomics datasets, including short-read RNA-seq, direct RNA sequencing, PAR-CLIP, DMS-MaP, and ribosome profiling (Ribo-seq).
• Develop new computational tools to study the dark transcriptome in cancer.
• Develop reproducible computational workflows, document and share code, and ensure the appropriate deposition of code and data in public repositories.
• Help manage the Insco Lab’s data, computational infrastructure, and bioinformatics resources.
• Prepare applications for individual fellowships, contribute to laboratory grant applications, and lead research projects.
• Think independently and creatively while collaborating effectively with the team.
• Mentor trainees in computational biology.
• Maintain detailed, well-organized, and accessible research records.
• Interpret and discuss research findings both informally and in lab meetings.
• Develop figures that clearly communicate research findings.
• Write original research manuscripts and review articles, and present findings at scientific conferences.

• Accomplished and motivated candidates with a PhD or equivalent doctoral degree in the biomedical, computational, or related sciences are encouraged to apply.
• Applicants should have experience analyzing complex transcriptomic or functional genomics datasets.
• Proficiency in Python and R, along with experience using high-performance computing environments to analyze large-scale biological datasets, is required.
• Experience in RNA biology, transcriptional biology, and/or cancer biology is desirable, and interest or experience in computational modeling of protein complexes is a plus.
• Candidates must demonstrate strong critical-thinking, scientific writing and communication skills, as well as a record of scientific…