Post Doc (0) - Hybrid - University of Massachusetts Medical School

General Summary of Position 

The Abdennur Lab at UMass Chan Medical School is seeking applications for a highly skilled and motivated Postdoctoral Researcher to join our dynamic team. The successful candidate will have a strong background in epigenetics and/or 3D genomics, computational biology, and machine learning. The successful candidate’s research will involve mining, exploration, integrative analysis, physical or statistical modeling, and visualization of large and multimodal data sets derived from next-generation sequencing technologies, leveraging both public data and experimental data from collaborators. This is an exciting opportunity to contribute to cutting-edge collaborative research with an emphasis on mapping and deciphering the biological roles of novel chromatin states. Opportunities for hands-on lab bench or software engineering experience are possible.  

About the Abdennur Lab 

We are a team of computational biologists and engineers developing both foundational and cutting-edge genomic data science tools and applying them to answer fundamental questions in 3D and functional genomics. We value tinkerers, interdisciplinary thinkers, and people with a passion to use their skills for scientific discovery. The Lab was founded in 2022 at the University of Massachusetts Chan Medical School, in the Department of Genomics and Computational Biology. We have active research collaborations with experimental investigators and are members of large collaborative initiatives, including the NIH 4D Nucleome (4DN) consortium and the newly established NIH Multi-Omics for Health and Disease (MOHD) consortium. 

About the PI 

Dr. Nezar Abdennur received his PhD in Computational and Systems Biology at MIT, specializing in the spatial organization of the genome (3C/Hi-C technologies), its relationship to the epigenome, and its influence on gene regulation. His postdoctoral work expanded to profile diversity in silent chromatin states and how they change in disease. His work has contributed to the discovery and understanding of the biophysical processes shaping genomes and has laid foundational open-source software for 3D genome analysis. He co-founded Open2C, a community of computational biologists developing open-source software for chromosome biology, with regular contributors from multiple institutions worldwide. He is also a co-developer of HiGlass, a visualization platform for interactive, multiscale, and multimodal exploration of (epi)genomic information, and Oxbow, a bioinformatic file interoperability library. 

Major Responsibilities: 

• Conduct independent computational biology research related to structural and functional genomics. 
• Develop and apply computational approaches to integrate, analyze, and predictively model large-scale bulk and single-cell omic datasets, including 3C/Hi-C, DNA methylation, histone modifications, and gene expression. 
• Collaborate with interdisciplinary teams to design and implement computational strategies for data integration, visualization, and interpretation. 
• Act as an ambassador for our group in relevant open-source communities, and foster collaborative projects to achieve shared goals. 
• Publish research findings in reputable scientific journals and present work at conferences. 
• Assist in mentoring and supervising graduate students and junior lab members. 

Qualifications: 

• Ph.D. in a relevant field (e.g., Computational Biology, Genomics, Physics, Computer Science, or a related discipline). 
• Background knowledge in epigenetic mechanisms, gene regulation, and genomic technologies. 
• Demonstrated track record of scientific productivity, including publications in reputable journals. 
• Experience in analyzing and interpreting high-throughput sequencing data, including but not limited to: Hi-C, ChIP-seq, RNA-seq, and WGBS. 
• Experience with relevant bioinformatics tools, workflows, genomic databases and software packages. 
• Hands-on experience with deep learning frameworks, including applying transformer models and LLMs. 

• Proficiency in Python and packages and data structures commonly used in computational biology and data science. 
• Ability to work independently as well as collaboratively in a team environment. 
• A history of, or interest in, contributing to open-source projects. 
• Any experience with either systems-level programming (e.g. Rust, C/C++) or frontend web frameworks is a plus. 

Application Instructions: 

Interested candidates should submit the following documents as a single PDF file to abdenlab@umassmed.edu 

• Cover letter describing research experience, interests, and career goals. 
• Curriculum Vitae (CV) including a list of publications. 
• Brief summary of previous research accomplishments (1-2 pages). 
• Contact information for three references. 

Review of applications will begin immediately and will continue until the position is filled. Only shortlisted candidates will be contacted for further evaluation and interview. 

SUPERVISION RECEIVED 

Under the direction of the PI. 

SUPERVISION EXERCISED 

Mentoring and supervising graduate students and junior lab members. 

ENVIRONMENTAL WORKING CONDITIONS 

Hybrid/remote work within the USA is allowed. Separate office space (to be shared with other members of the lab) for on-site work.