REQUIRED PROFESSIONAL SKILLS:

• HIPAA and CITI certifications must be obtained and maintained in accordance with university and sponsor policies.

DYNANET TEAM REQUIREMENTS AND EXPECTATIONS:

• Possess Strong written and verbal communication skills.
• Highly organized with an ability to prioritize, balance, and effectively advance multiple competing priorities in a high-volume, fast-paced environment.
• Ability to interact in a professional and collaborative manner with fellow Dynanet Teammates and the clients, and business partners that we work with.
• Ability and desire to challenge and educate yourself to support and advance IT services delivery in the Federal agencies we serve.
• Excellent judgment and creative problem-solving skills.
• Respond to team member and client requests via email, MS teams, or other communication means during core business hours.
• Active listening skills to understand clients’ needs, and collaboration skills to work with other developers and designers.

EDUCATION/EXPERIENCE REQUIREMENTS:

• PhD in Bioinformatics, Computational Biology, Biomedical Engineering, Data Science, or a related field.
• Demonstrated experience in spatial omics data analysis (e.g., 10X Visium, Xenium).
• Proficiency with single-cell RNA-seq tools and workflows (e.g., Seurat, Scanpy).
• Knowledge of machine learning or foundational modeling applied to biomedical data.
• Strong publication record in computational biology, systems biology, or AI for healthcare.
• Experience in high-dimensional data integration and reproducible workflow development.
• Requires a high level of independence, strong computational skills, and the ability to contribute to multi-disciplinary team science.
• Must demonstrate initiative, attention to detail, and a commitment to open, reproducible research practices.

ABOUT THE ROLE:

This Postdoctoral Associate will join the Computational Microscopy Imaging Lab (CMIL), led by Dr. Pinaki Sarder, and contribute to a federally funded R01 initiative focused on the integration of spatial omics, transcriptomics, and histology data. The role emphasizes computational analysis of single-cell RNA sequencing data and cutting-edge spatial omics data generated using technologies such as 10X Visium and Xenium. The researcher will lead efforts in integrating multi-modal biomedical data with histological and clinical features using machine learning and foundational modeling approaches. This work supports disease modeling across chronic kidney disease, acute kidney injury, cancer, and neurological conditions. A strong publication record and background in computational biology, molecular omics, and AI are essential.
Requirements: