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, with the goal of unlocking new therapeutic strategies for children with cancer. 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.
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Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree