• Lead independent computational/AI research projects focused on identifying and prioritizing candidate targets for emerging bispecific therapeutic strategies in pediatric cancers.
• Develop AI and agent-based workflows integrating large-scale single-cell, spatial transcriptomic, bulk genomic, functional-genomic, and pharmacologic datasets to nominate disease-selective vulnerabilities and target combinations.
• Nominate disease-selective cell-surface antigens and antigen combinations based on malignant-cell specificity, co-expression, spatial localization, normal-tissue expression, targetability, and therapeutic rationale.
• Strong background in quantitative/computational biology or a related field.
• Very strong commitment to rigor and scientific integrity.
• Proficiency in a major programming language used for data analysis, such as R or Python.
• Experience with single-cell or spatial transcriptomic analysis is desirable.
• Experience with machine learning, LLMs, AI agents, multimodal data integration, or automated biological interpretation workflows is desirable but not required.
• Experience leading or substantially driving a complex research project (evidenced by e.g. a first author paper or similar tangible contribution)
Email CV to postdoc@stjude.org.