Hybrid Postdoctoral Associate – Computational Spatial Omics en Dynanet Corporation

Description

Position Details: 

Job Title: Postdoctoral Associate – Computational Spatial Omics 

Job Type: Full-time 

Location: Gainesville, Florida - Onsite

Revised: 8/18/2025 

Dynanet Corporation Overview: 

Dynanet started with a focus on IT infrastructure and operations, helping organizations enhance their networks and overcome the limitations of 1990s technology. From strengthening communication channels to introducing innovative ways to collaborate and share information, Dynanet played a crucial role in shaping the early stages of digital transformation. The company’s efforts helped organizations build the very fabric of connectivity that now powers our modern world. Over the last three decades, Dynanet has grown into a trusted partner for organizations looking to innovate boldly and transform seamlessly. While technology continues to evolve and unlock new opportunities, for nearly 30 years, Dynanet remains committed to delivering cutting-edge solutions that drive lasting change for its customers. Through agility, foresight, and an unwavering dedication to excellence, Dynanet continues to empower organizations to thrive in a rapidly changing digital landscape. Our story is more than just a story of technology – it’s a story of vision, growth, and transformation that has shaped the past and continues to pave the way for the future. 

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

Roles & Responsibilities: 

ESSENTIAL FUNCTIONS OF THE JOB AND THE PERCENTAGE OF TIME SPENT ON EACH FUNCTION: 

Spatial Omics Data Processing and Analysis – 30% 

• Analyze spatial transcriptomics data using platforms such as 10X Visium and Xenium. 
• Perform quality control, normalization, spatial clustering, and feature extraction. 
• Apply tools for spatial gene expression visualization and interpretation. 

Single-Cell RNA-Seq Data Integration – 20% 

• Process and analyze single-cell RNA sequencing data to support integration with other modalities. 
• Conduct clustering, annotation, and alignment with spatial and histological data. 
• Assist with incorporating scRNA-seq results into multi-modal pipelines. 

Multi-Modal Data Integration and Modeling – 20% 

• Develop and implement computational workflows for integrating omics, histology, and clinical data. 
• Apply machine learning and foundational modeling to support predictive or exploratory analyses. 
• Collaborate with interdisciplinary teams to refine multi-modal pipelines. 

Scientific Writing and Publications – 10% 

• Lead or co-author research manuscripts derived from spatial omics and integration analyses. 
• Prepare figures, methods, and documentation for publication in peer-reviewed journals. 
• Support grant progress reports and contribute to collaborative manuscript efforts. 

Collaboration and Reporting – 10% 

• Participate in research meetings, cross-institution collaborations, and project planning. 
• Document analysis pipelines and maintain clear records of research activities. 
• Communicate project progress and key findings to the PI and collaborators. 

Mentorship and Junior Staff Support – 10% 

• Guide junior analysts, students, or interns on tools, tasks, and workflow execution. 
• Assist with onboarding and ensure alignment with lab goals and project deliverables. 
• Offer informal mentoring to promote skill development and consistency in data handling. 

MARGINAL FUNCTIONS OF THE JOB AND THE PERCENTAGE OF TIME SPENT ON EACH FUNCTION: 

Scientific Communication Support – 5% 

• Assist with preparing slides, posters, and internal presentations for conferences or research meetings. 
• Help communicate complex analytic processes to interdisciplinary audiences. 

Tool Exploration and Workflow Refinement – 5% 

• Test new spatial omics or integration tools for performance and relevance to lab needs. 
• Propose enhancements to improve scalability, accuracy, or reproducibility in data pipelines. 

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.  

Employee Benefits Overview: 

• Industry Competitive Compensation 
• Medical and Dental Insurance 
• Paid Time Off/Holidays 
• 401(k) Retirement Plans with Matching 
• Paid Training 
• Employee Referral Program 
• Employee Development Program