Post Doctoral Associate

The University of Pittsburgh, Department of Medicine, Division of Hematology Oncology is seeking a postdoctoral associate. The post-doc associate will be conducting research to study the role and mechanism of m6A transcriptome methylation in cancer using a combination of statistical/machine learning and high throughput methods. The postdoc associate will have close interactions with the PI, fellow lab members, and cancer biology/clinical collaborators including daily interactions with the PI, regular lab meetings, and meetings with collaborators. The post-doc associate will also have the experience to mentor graduate students in the lab.

Candidate must have a Ph.D. in Bioinformatics, Computer Science, Biostatistics, or a related field. Preferred Qualifications, must have strong AI/bioinformatics/statistical learning skills with experience in model development and model in AI and deep learning models. Experienced in analysis of high-throughput sequencing data including (sc)RNA-seq, microbiome sequencing, etc. Strong background in molecular biology.

Research will focus on studying mechanisms of infection and oncogenesis by cancer viruses, especially Kaposi's sarcoma-associated herpesvirus (KSHV) Nat. cell Biology 2010, Nat. Microbiology 2018, MBIO 2019, PLOS Pathogens 2020), m6A mRNA epitranscriptome and cancer, spatially-resolved single-cell transcriptomics analysis (JMV, 2023), and functional interpretable deep learning models and large language models for cancer genomics and biomedical research. Motivated individual to work on 1) Develop bioinformatics/machine learning tools to facilitate the functional study of m6A mRNA methylation and cancer; 2) Develop AI/machine learning tools for modeling tissue structure, disease pathology, and immune microenvironments from spatially-resolved single-cell transcriptomics data (Nanostring CosMx, 10X Xenium).

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