Postdoctoral Fellow: Cancer ML & Molecular Data Science

Moffitt Cancer Center; Tampa (FL); On-site; USD 73,000 - 102,000; Full time

Benefits offered by this job

Medical coverage

Dental & Vision insurance

Paid time off

Retirement plan

Relocation assistance

Job summary

The Moffitt Cancer Center in Tampa, Florida, invites applications for a Postdoctoral Fellow to advance machine learning, artificial intelligence, and molecular data science in cancer research. The role focuses on representation learning, foundation models, predictive modeling, and multimodal biomedical analysis using large-scale molecular and clinical datasets.

Your work will involve self-supervised learning, transformer-based architectures, graph methods, and generative models, collaborating

Qualifications

• PhD or equivalent in CS, ECE, math, physics, or a related quantitative field.
• Strong publication record with at least one first-author peer-reviewed publication.
• Experience with ML/AI in biomedical data.
• Excellent written and verbal communication skills.

Responsibilities

• Develop ML models for molecular and multi-omics data analysis.
• Design scalable pipelines for molecular data integration and feature representation.
• Investigate and apply advanced AI approaches (foundation models, self-supervised learning, transformers).
• Collaborate with researchers, clinicians, and data scientists on translational cancer projects.
• Present findings at scientific meetings and contribute to manuscripts and grants.

Skills

Python

PyTorch

Machine learning

Self-supervised learning

Transformer models

Graph-based methods

Data analysis

Communication skills

Education

PhD in CS / ECE / Math / Physics / Bioinformatics / Computational Biology

Tools

Git

High-performance computing

Jupyter Notebook

Job description

The Moffitt Cancer Center in Tampa, Florida, invites applications for a Postdoctoral Fellow to advance machine learning, artificial intelligence, and molecular data science in cancer research. The role focuses on representation learning, foundation models, predictive modeling, and multimodal biomedical analysis using large-scale molecular and clinical datasets.

Your work will involve self-supervised learning, transformer-based architectures, graph methods, and generative models, collaborating