Post Doc

Postdoctoral Positions In Deep-Learning Omics

Postdoctoral positions in Deep-Learning Omics are available in the Zhou Lab at UMass Chan Medical School. The Zhou Lab develops and applies cutting-edge computational and big-data approaches to understand the genomics, epigenomics, and regulatory functions of noncoding RNAs in human disease. We develop computational methods and scalable pipelines to decode noncoding RNAs and their epigenetic modifications from diverse high-throughput sequencing data, including bulk, single-cell, and long-read sequencing data.

Lab Research:

• AI-Driven Algorithms & Software: Develop deep learning/machine learning/statistical based algorithms to elucidate lncRNAs, fusion transcripts, RNA modifications, and circular RNAs in human genetics and disease, toward developing RNA-based therapeutics.
• Integrative Analysis of Multi-Omics Data: Build end-to-end workflows for bulk and single-cell RNA-seq, long-read sequencing, and epigenomic assays, enabling efficient processing of large-scale multi-omics datasets, and apply them to decipher human diseases.
• Translational Discovery: Integrate computational findings with clinical and genetic data using machine learning/deep learning methods to identify RNA signatures of disease, guiding the development of RNA-based diagnostics and precision-medicine strategies.

The successful candidate will benefit from collaboration with world-class scientists and physicians at UMass Medical Center and in the Greater Boston area, including Harvard/MIT and other prestigious institutes. Dr. Zhou provides tailored mentorship and resources to align postdoctoral training with career goals, whether in academia, industry, or beyond.

Required Qualifications:

• Ph.D. (or imminent defense) in Computational Biology, Bioinformatics, Computer Science, Applied Mathematics, Statistics, Biophysics, Biomedical Engineering, or a related field.
• Strong programming skills in at least one of:
  Python, R, C/C++, or Perl; proficient with Unix/Linux.
• Experience with deep learning, statistical modeling, or AI applications to biological data.
• Familiarity with transcriptomic technologies, ideally single-cell RNA-seq or long-read sequencing.
• Demonstrated ability to conduct independent research and publish as first/co-author.
• Excellent communication, teamwork, and project-management skills.

Application Instructions:

• Cover Letter: Summarize your research background, interests, and career objectives.
• Curriculum Vitae: Include publication list and detailed computational skill set.
• References: Contact information for at least two referees (email and phone).

Equal Opportunity Employer

UMass Chan Medical School is committed to fostering a diverse and inclusive environment. All qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.