Post Doctoral Associate - Military veterans preferred

Post Doctoral Associate
Med-Biomedical Informatics - Pennsylvania-Pittsburgh - (25003252)

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.

Key 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.

Qualifications: The ideal candidate will have a Ph.D. in Bioinformatics, Biostatistics, Computational Biology, Computer Science or a related field, with a strong background in bioinformatics/statistical methods and data analysis. Previous experience with the following is highly desirable:

• Data Processing: 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.
• Cloud computing: Advanced expertise in cloud computing platforms (AWS, Google Cloud and HTC), containerization technologies, and infrastructure-as-code frameworks, demonstrating proficiency in deploying, managing, and optimizing scalable computational workflows for processing large-scale genomic and multiomics datasets in a secure, cost-effective environment.
• Data Analysis: Expertise in applying bioinformatics approaches to analyze complex datasets, including model fitting, statistical testing, and data visualization in R and/or Python. Ability to work closely with experimentalists to ensure analyses complement experimental goals and enhance data interpretation.
• Machine learning/Deep learning: Expertise in developing and implementing sophisticated machine learning and deep learning models (such as neural networks, random forests, and ensemble methods) for predictive biomarker discovery and outcome prediction using high-dimensional molecular data (genomic, transcriptomic, proteomic) and/or medical imaging datasets, with proven ability to preprocess complex biological data, perform feature selection, address class imbalance, and validate models through rigorous statistical methods including cross-validation and external dataset testing.
• Advanced Computational Skills: Proficiency in bioinformatics tools and libraries (e.g., Bioconductor, STAR, DESeq2, Seurat).

Location: This position offers flexible remote work arrangements, allowing the successful candidate to work from a location of their choice (US only) while maintaining productivity through virtual collaboration tools. The role may require occasional in-person meetings or lab visits for specific project milestones, training sessions, or team-building events, with frequency determined based on project needs.

APPLICATION REQUIREMENTS

Document requirements

• Curriculum Vitae - Your most recently updated C.V.
• Cover Letter

Reference requirements

• 2-4 required (contact information only)

References may be contacted to verify experience.

Interested candidates should submit a brief cover letter, curriculum vitae (CV), and contact information for at least two references to Dr. Lujia Chen (luc17@pitt.edu).

The University of Pittsburgh is committed to championing all aspects of diversity, equity, inclusion, and accessibility within our community. This commitment is a fundamental value of the University and is crucial in helping us advance our mission, which includes attracting and retaining diverse workforces. We will continue to create and maintain an environment that allows individuals to discover, belong, contribute, and grow, while honoring the experiences, perspectives, and unique identities of all.

The University of Pittsburgh is an Affirmative Action/Equal Opportunity Employer and values equality of opportunity, human dignity and diversity. EOE, including disability/vets.

Assignment Category: Full-time regular
Campus: Pittsburgh
Child Protection Clearances: Not Applicable
Required Attachments: Cover Letter, Curriculum Vitae

Assignment Category Full-time regular



Equal employment opportunity, including veterans and individuals with disabilities.

PI276834830