Postdoctoral Fellow - Computational Biology

City of Hope • Duarte, CA

7d • Onsite

About The Position

Join the forefront of groundbreaking research at City of Hope where we're changing lives and making a real difference in the fight against cancer, diabetes, and other life-threatening illnesses. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases, shaping the future of medicine through cutting-edge research.
The Department of Diabetes and Cancer Metabolism at City of Hope is seeking a highly motivated Postdoctoral Fellow in Computational Biology/Bioinformatics to join an NIH R01-funded research program focused on decoding cardiomyocyte subtype dynamics and molecular mechanisms underlying heart failure progression.
Led by Didi Ren, Ph.D., Principal Investigator, the lab integrates single-cell and single-nucleus multi-omics, machine learning/AI, computational biology, and experimental validation to understand cardiovascular disease progression and identify novel therapeutic targets. The successful candidate will primarily lead computational and bioinformatics studies within the lab and will have opportunities to develop new analytical approaches, publish high-impact research, collaborate with multidisciplinary investigators, and establish an independent research direction.

Requirements

• PhD or M.D. in bioinformatics, computational biology, genomics, computer science, biostatistics, biomedical sciences, or a related field.
• Strong experience in bioinformatics and analysis of high-dimensional genomic datasets.
• Strong programming skills in Python and/or R, and experience with Linux/HPC computing environments.
• Experience with single-cell RNA-seq, single-nucleus RNA-seq, sc/snATAC-seq, or multiome analysis.
• Strong analytical, problem-solving, scientific writing, and communication skills.

Nice To Haves

• Experience with machine learning, deep learning, or foundation models applied to biomedical data.
• Experience or strong motivation in developing LLM-based applications, AI agents, multi-agent systems, RAG, tool-calling workflows, or autonomous scientific workflows.
• Familiarity with modern AI frameworks and APIs for developing agentic applications.
• Experience with multi-omics integration, gene regulatory networks, spatial transcriptomics, trajectory analysis, or chromatin accessibility analysis.
• Experience in cardiovascular biology or heart failure is preferred but not required.
• Demonstrated ability to independently develop computational methods and drive research projects from conception through publication.

Responsibilities

• Lead computational analysis of single-cell/single-nucleus RNA-seq, ATAC-seq, and multiome datasets related to heart failure and cardiovascular biology.
• Develop and apply machine-learning and deep-learning approaches to identify disease-associated cardiomyocyte subtypes, cellular trajectories, and molecular drivers.
• Develop AI agents and LLM-based computational workflows for biomedical research, including automated dataset discovery, quality assessment, multi-omics analysis, biological interpretation, and hypothesis generation.
• Integrate transcriptomic, epigenomic, spatial transcriptomic, and other multi-omics datasets.
• Perform gene regulatory network, transcription factor, pathway, and cell-state analyses.
• Develop reproducible bioinformatics pipelines and computational tools using Python and/or R.
• Work closely with Dr. Ren and experimental and computational collaborators to translate computational discoveries into experimentally testable hypotheses.
• Contribute to manuscripts, conference presentations, grant-related research, and development of new research directions.

Benefits

• Comprehensive Benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree