Postdoctoral Research Associate – Geeleher Lab (dry lab)

Position Responsibilities

• 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.
• Work closely with wet-lab scientists to design follow-up validation studies and interpret experimental results in the context of large-scale datasets.
• Generate publication-quality analyses, figures, and visualizations, and contribute to study design, data interpretation, and project strategy.
• Lead projects toward publication, including drafting manuscripts, preparing methods and results sections, and responding to reviewer comments.
• Present research findings at lab meetings, institutional seminars, collaborative meetings, and scientific conferences.
• Mentor junior lab members and contribute to a collaborative wet-dry lab environment.

Special Skills, Knowledge, and Abilities

• 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.
• Ability to rapidly learn, optimize, and implement new or advanced techniques as required by the evolving research direction.
• Experience leading or substantially driving a complex research project (evidenced by e.g. a first author paper or similar tangible contribution).
• Scientific writing skills, including experience contributing to or drafting manuscripts.
• Experience in pediatric cancer is desirable but not required.
• Strong written and spoken English, presentation, and communication skills.

Minimum Education and/or Training

Ph.D. degree or equivalent in Computational Biology, Genetics/Genomics, Bioinformatics, Computer Science, Mathematics, Physics, or related field.