The Zhang Lab within the section of computational biomedicine at Boston University Chobanian & Avedisian School of Medicine is seeking multiple Computational Biology Postdoctoral Associates interested in studying immuno-oncology, kidney disease, or aging. Postdoctoral scholars will lead the development of novel computational and bioinformatics methods and integrate diverse types of high-throughput biological, omics, and biomedical imaging data generated by the lab and its collaborators.

The position offers dedicated funding for computational equipment and support to attend national and international conferences. The lab provides an interdisciplinary research environment with opportunities to develop innovative computational methods and apply them to important biological and biomedical questions.

Responsibilities:

• Develop principled methods, based on deep learning and advanced mathematical modeling techniques to address biological questions.
• Analyze massive functional genomics data at single cell and population levels.
• Analyze large cohorts of pathological imaging and spatial transcriptomics data.
• Prepare manuscripts and scientific reports.

Required Skills

Qualifications

• PhD in Computational Biology, Computer Science, Biostatistics, Mathematics or other related quantitative areas.
• Experience with teamwork and group communication.
• Experience with self-guided learning in a quickly evolving technical environment.
• Experience in kidney disease, melanoma, AD, microbiome, or other relevant disease areas is preferred.

Additional Preferred Qualifications Related to Potential Projects:

Microbiome and metagenomics: Familiarity with 16S rRNA or shotgun metagenomic data analysis, commonly used microbiome identification and profiling tools, and microbiome metabolic analysis. Experience studying microbiome-host or microbiome-microenvironment interactions is preferred.

Biomedical imaging analysis: Experience with biomedical image analysis, including histopathology images (e.g., H&E), spatial transcriptomics, image processing, segmentation, and classification. Experience with multimodal data integration, such as integrating imaging and molecular data or combining different imaging modalities, is highly desirable. Experience with image registration and 3D reconstruction is preferred.

Deep learning and mathematical modeling: Strong background in deep learning and/or mathematical modeling, with familiarity with graph neural networks, diffusion models, flow matching, and optimal transport.

To Apply:

Please send a letter of interest, current CV and the names and contact information for three references to Dr. Chao Zhang at chz2009@bu.edu.

_We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, natural or protective hairstyle, religion, sex, age, national origin, physical or mental disability, sexual orientation, gender identity, genetic information, military service, pregnancy or pregnancy-related condition, or because of marital, parental, or veteran status. We are a VEVRAA Federal Contractor. _

Required Experience

Qualifications

• PhD in Computational Biology, Computer Science, Biostatistics, Mathematics or other related quantitative areas.
• Experience with teamwork and group communication.
• Experience with self-guided learning in a quickly evolving technical environment.
• Experience in kidney disease, melanoma, AD, microbiome, or other relevant disease areas is preferred.

Additional Preferred Qualifications Related to Potential Projects:

Microbiome and metagenomics: Familiarity with 16S rRNA or shotgun metagenomic data analysis, commonly used microbiome identification and profiling tools, and microbiome metabolic analysis. Experience studying microbiome-host or microbiome-microenvironment interactions is preferred.

Biomedical imaging analysis: Experience with biomedical image analysis, including histopathology images (e.g., H&E), spatial transcriptomics, image processing, segmentation, and classification. Experience with multimodal data integration, such as integrating imaging and molecular data or combining different imaging modalities, is highly desirable. Experience with image registration and 3D reconstruction is preferred.

Deep learning and mathematical modeling: Strong background in deep learning and/or mathematical modeling, with familiarity with graph neural networks, diffusion models, flow matching, and optimal transport.

To Apply:

Please send a letter of interest, current CV and the names and contact information for three references to Dr. Chao Zhang at chz2009@bu.edu.

_We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, natural or protective hairstyle, religion, sex, age, national origin, physical or mental disability, sexual orientation, gender identity, genetic information, military service, pregnancy or pregnancy-related condition, or because of marital, parental, or veteran status. We are a VEVRAA Federal Contractor. _