The Computational Microscopy Imaging Lab (CMIL) needs to integrate biomedical imaging, spatial omics, and clinical data into scalable research infrastructure to support NIH-funded research initiatives. This involves harmonizing complex data sources, applying advanced statistical methods, and developing reproducible computational pipelines for translational research.
Requirements
- Expertise with spatial omics platforms (e.g., Visium, CODEX), digital pathology, and histological imaging
- Proficiency in Python, GitHub, and high-performance computing systems (e.g., HiPerGator)
- Experience with secure software design, MLOps practices, and containerization tools (Docker, Singularity)
- Familiarity with statistical modeling, feature engineering, and data visualization
- A Bachelor's Degree in data science, statistics, bioinformatics, analytics, or similar field and seven years of experience; Master's Degree in data science, statistics, bioinformatics, analytics, or similar field and five years of experience; Doctoral Degree in data science, statistics, bioinformatics, analytics, or similar field and three years of experience.
Responsibilities
- Architect and lead the development of scalable pipelines for processing histology, spatial omics, and clinical datasets
- Ensure analytic workflows are modular, reproducible, and version-controlled using GitHub
- Oversee optimization of pipeline execution on high-performance computing platforms such as HiPerGator
- Apply and adapt statistical modeling techniques to multi-modal biomedical datasets
- Lead efforts in feature extraction and harmonization for machine learning integration
- Design and maintain advanced tools, APIs, and visualization interfaces that support research scalability
- Implement best practices in software architecture, testing, and secure deployment
Other
- This position also includes mentoring junior data analysts, student researchers, and technical interns.
- Provide structured mentorship to junior staff, graduate students, and interns.
- Help shape long-term strategic planning for data standards and technical capabilities.
- Strong interpersonal skills and experience collaborating in interdisciplinary research settings
- Serve as a technical liaison to translate scientific objectives into executable data strategies
