Postdoctoral Fellow - Radiation Oncology - Research

The Postdoctoral Fellow will serve as a technical research and data-engineering lead supporting large-scale, NIH-funded translational oncology studies, including the OPULENCE R01 program, which focuses on developing multimodal data standards and predictive models of oral and dental toxicities experienced by patients with head and neck cancers (HNC) who are treated with radiation therapy.

This role is best suited for a proactive individual who demonstrates strong analytical thinking skills necessary to tackle challenging data science problems and who enjoys finding innovative solutions towards building efficient data systems, pipelines, and standards. The postdoctoral fellow will design, implement, and maintain research-grade data infrastructure spanning clinical, imaging, patient-reported outcomes (PROs), dental/oral health, biospecimens, and derived AI/ML features, using both structured and unstructured data sources.

The position blends research operations, data engineering, and informatics, with opportunities to contribute to ontology development, LLM-enabled data extraction, and advanced analytics pipelines in collaboration with clinicians, dentists, physicists, informaticians, and data scientists. Moreover, this position provides opportunities for manuscript writing, grantsmanship, and advancement in associated research career trajectories.

All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.

• Research Data Engineering & Systems Development
  • Design, build, and maintain production-quality research data pipelines supporting prospective and retrospective oncology cohorts.
  • Implement ETL / ELT workflows to ingest, transform, validate, and harmonize structured and unstructured data from:
  • Electronic health records (EHR)
  • Imaging metadata and derived features
  • Patient-reported outcomes (ePROs)
  • Dental and oral health assessments
  • Research databases and external registries
  • Proactively identify opportunities to improve data quality, completeness, and reproducibility across research workflows.
  • Serve as a technical resource for data model design, schema evolution, and versioning.

• Database, Ontology, and Standards Development
  • Support the development and maintenance of research ontologies and common data elements (CDEs) aligned with national standards (e.g., clinical, imaging, and outcomes domains).
  • Translate existing research data models into ontology-based representations to support analytics, interoperability, and AI workflows.
  • Document data schemas, ontologies, transformations, and analytical assumptions to support transparency and reuse.
  • Collaborate with investigators to refine data structures that improve extensibility, semantic clarity, and downstream analysis.

• Advanced Analytics, AI/ML, and LLM Enablement
  • Prepare structured and unstructured datasets for predictive, descriptive, and exploratory modeling, including AI/ML and statistical analyses.
  • Support LLM-based workflows for extraction of clinical concepts from free-text (e.g., clinical notes, imaging reports, pathology reports).
  • Assist with feature engineering, cohort construction, and data serialization for modeling and visualization platforms.

• Platform & Tooling (Foundry Desired, Not Required)
  • Build and manage data assets using modern analytics platforms; experience with Palantir Foundry is desired but not required.
  • For Foundry users:
    • Create and maintain backing datasets, transformations, and ontology objects
    • Implement data validations, permissions, and pipeline monitoring
    • Design and deploy interactive, ontology-driven workflow-specific Workshop Apps
  • For non-Foundry users:
    • Apply equivalent best practices using relational databases, Python/SQL workflows, and cloud or on-prem research environments.

• Collaboration, Documentation, and Research Operations
  • Work closely with clinicians, research coordinators, statisticians, and informatics teams to translate scientific questions into data solutions.
  • Produce clear technical documentation (data dictionaries, pipeline descriptions, SOPs).
  • Support IRB-compliant data governance, including secure handling of…