Analyze single-cell and multiomics datasets to extract biological insights supporting precision medicine and drug development programs
Apply and evaluate machine learning and deep learning approaches to single-cell data for tasks such as cell type classification, biomarker discovery, and patient stratification
Explore and prototype generative AI and LLM-based approaches to accelerate biological data interpretation and scientific workflows
Collaborate with scientists, clinicians, and data scientists to design and execute data-driven research projects
Document and optimize computational workflows following reproducible research best practices
Present findings through technical reports, visualizations, and presentations to cross-functional teams

Requirements

Current Ph.D. candidate in Bioinformatics, Computational Biology, Computer Science, Biostatistics, or a related quantitative field
Single-cell omics experience: Demonstrated ability to process, analyze, and interpret single-cell data (scRNA-seq, scATAC-seq, CITE-seq, or spatial transcriptomics) using frameworks such as Scanpy/scverse, Seurat, or Bioconductor
Machine learning expertise: Applied experience developing and evaluating ML/deep learning models on biological data, including neural network architectures (GNNs, transformers, autoencoders), model selection and benchmarking, and integration of ML approaches into analytical workflows
Programming proficiency: Python and/or R for data analysis, statistical modeling, and visualization
Statistical foundation: Understanding of statistical methods for biological data (hypothesis testing, differential expression, multiple testing correction, clustering)
Strong problem-solving skills and ability to communicate complex insights effectively.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

single-cell analysismultiomics datasetsmachine learningdeep learninggenerative AILLM-based approachesPythonRstatistical modelingneural network architectures

Soft Skills

problem-solvingcommunicationcollaborationdata-driven researchtechnical reportingvisualizationpresentation skillsworkflow optimizationreproducible researchcross-functional teamwork