Post Doctoral Associate

University of Pittsburgh • Pittsburgh, PA

141d

About The Position

Dr. Lujia Chen's lab in the Department of Biomedical Informatics at the University of Pittsburgh is seeking a highly motivated and skilled Postdoctoral Fellow/Research Scientist in Bioinformatics to lead bioinformatics/machine learning/deep learning/AI-driven discovery of cell-cell communication in the tumor microenvironment and discovery of predictive/prognostic biomarkers of various drugs aimed at advancing precision medicine. The chosen candidate will play a vital role in developing and implementing translational research, contribute to prestigious journal publications and funding applications, and have opportunities to guide early-career researchers in computational methods and bioinformatics. This role receives funding through the Principal Investigator's NIH R01 grant.

Requirements

• Ph.D. in Bioinformatics, Biostatistics, Computational Biology, Computer Science or a related field.
• Strong background in bioinformatics/statistical methods and data analysis.
• Proficiency in analyzing high-throughput sequencing data, including Whole Exome Sequencing, RNA sequencing, single-cell RNA sequencing and spatial transcriptomics.
• Familiarity with the use of version control (Git) and virtual environments (e.g., conda, Docker) to manage and improve computational pipelines.
• Advanced expertise in cloud computing platforms (AWS, Google Cloud and HTC), containerization technologies, and infrastructure-as-code frameworks.
• Expertise in applying bioinformatics approaches to analyze complex datasets, including model fitting, statistical testing, and data visualization in R and/or Python.
• Expertise in developing and implementing sophisticated machine learning and deep learning models for predictive biomarker discovery and outcome prediction.
• Proficiency in bioinformatics tools and libraries (e.g., Bioconductor, STAR, DESeq2, Seurat).

Responsibilities

• Develop, optimize, and manage bioinformatics pipelines for processing and analyzing large-scale sequencing data (e.g., whole exome sequencing, RNA sequencing, single-cell RNA sequencing).
• Develop machine learning and deep learning models to study the cell-cell communication in the tumor microenvironment.
• Develop machine learning and deep learning models to identify predictive/prognostic biomarkers for various drugs by collecting and applying multiomics data.
• Collaborate with experimentalists and clinical researchers to design integrative computational analyses that inform experimental strategies and therapeutic interventions.
• Contribute to high-impact publications, present findings at conferences, and assist in the preparation of grant proposals.
• Mentor and guide PhD/Master students in the lab, promoting technical and career development.

Benefits

• Flexible remote work arrangements, allowing the successful candidate to work from a location of their choice (US only).
• Occasional in-person meetings or lab visits for specific project milestones, training sessions, or team-building events.

What This Job Offers

• Career Level: Entry Level
• Education Level: Ph.D. or professional degree