The Geeleher Lab tightly integrates computational/AI-based analysis of high-throughput genomics datasets (e.g. single-cell / spatial genomics, functional screens) with wet-bench experimental work. We ultimately aim to improve outcomes for children with cancer, with a particular focus on neuroblastoma and other high-risk pediatric solid tumors. Our hybrid wet-dry lab has led publications in journals including Nature, Genome Biology, the Journal of the National Cancer Institute, and Nature Communications, and is supported by NIH funding, including R01 (NCI renewal recently scored 1st percentile) and R35 awards, as well as institutional funding from ALSAC.

We are seeking a dry-lab postdoctoral scientist to lead computational and AI/ML-driven efforts to identify therapeutic target pairs from atlas-scale pediatric single-cell and spatial transcriptomic datasets. We are particularly interested in developing AI- and agent-based approaches to nominate cell-surface antigen combinations for emerging dual-targeted and logic-gated therapeutic strategies, including AND-gated bispecific antibody-drug conjugates and logic-gated cellular therapies. Pediatric cancers are especially well suited to these approaches because many are driven by aberrant developmental or ectopic transcriptional programs that generate highly disease-selective cell states. However, systematic efforts to identify and prioritize such target pairs at scale remain very limited, creating substantial scope for discovery.

Our integrated wet-dry lab is particularly well positioned to move prioritized candidates through experimental validation and preclinical development. The successful candidate will also benefit from co-mentorship by Anand Patel, M.D., Ph.D., a pediatric oncologist in the St. Jude Department of Oncology with expertise in translating genomics-derived discoveries from bench to bedside, further supporting the goal of advancing new therapeutic strategies for children with cancer.

Examples of recent representative papers led by dry-lab scientists in our lab (listed as first author), with strong

ML/AI components include:

https://www.biorxiv.org/content/10.64898/2026.03.04.709438v2

https://link.springer.com/article/10.1186/s13059-024-03309-4

https://www.cell.com/cell-genomics/fulltext/S2666-979X(24)00368-9

https://academic.oup.com/nar/article/50/14/e80/6583238

https://www.nature.com/articles/s41467-025-66223-8

We also routinely publish tightly integrated wet-lab/computational studies, with recent first authorships by Geeleher Lab members, including:

https://www.nature.com/articles/s41467-021-26640-x

https://www.nature.com/articles/s41467-023-43134-0

https://www.nature.com/articles/s41467-025-57185-y

The candidate will be strongly supported in their career objectives, regardless of whether their goals are academic or industry, and will be supported in writing grants/fellowships if they are interested in the academic faculty path. This position is located in Memphis, TN (100% on-site position), and relocation assistance is available. Salary and benefits follow the (highly competitive) St. Jude postdoctoral compensation scale (www.stjude.org/postdoc; Note: starting salary for this position is approximately $85k, i.e. higher than stated in the link).

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).

Minimum Education and/or Training:

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

· 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.

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