Applied Machine Learning Postdoctoral Research Fellow

Moffitt Cancer Center

9d • $72,639 - $101,695

About The Position

Dr. Ghulam Rasool seeks an Applied Postdoctoral Research Fellow to develop and apply advanced AI methods for multimodal cancer patient modeling. Research will focus on federated learning across distributed clinical data environments, with applications including matching patients to clinical trials, predicting treatment response and outcomes, stratifying risk, and personalizing cancer care.

The position offers the opportunity to work in a research environment with state-of-the-art facilities and collaborations with researchers and clinicians across disciplines. The fellow will develop machine learning models for integrating multiscale, heterogeneous cancer datasets and apply machine learning approaches in preclinical and clinical model development. Moffitt combines patient care with clinical and basic research, offering opportunities for translational research. Expert faculty provide mentorship, including through funded research programs and T32 Training Grants, and fellows are encouraged and supported to apply for training grants.

Applicants should submit one PDF containing a cover letter summarizing their research training and accomplishments, a personal statement describing research interests and goals, a current CV with recent publications, and contact information for three references to Dr. Rasool.

Requirements

• PhD or equivalent degree in Electrical, Biomedical and/or Computer Engineering, Computer Sciences, Mathematics, Physics, or a related discipline.
• At least one first-author publication.
• Expertise in natural language processing (NLP), large language models (LLMs), medical imaging, and multimodal machine learning, including experience integrating structured and unstructured clinical data such as electronic health records (EHR), clinical text, medical imaging, pathology, and molecular data.

Nice To Haves

• Experience with federated learning, privacy-preserving AI, clinical prediction, patient-trial matching, and modern deep learning frameworks is highly desirable.

Responsibilities

• Develop machine learning models using Transformers and/or GNNs to integrate multimodal cancer data, including radiology, pathology, and clinical data.
• Develop image, text, and tabular data-processing pipelines for machine learning models.
• Develop privacy-preserving and federated learning approaches across distributed clinical data environments to support patient-trial matching, treatment response and outcome prediction, risk stratification, and personalized cancer care.
• Collaborate with team members to develop and implement technologies in preclinical and clinical settings.
• Contribute to manuscript writing and generate data for grant applications.

Benefits

• Comprehensive medical coverage, with Moffitt covering the premium effective the first day.
• Dental insurance.
• Vision insurance.
• Generous paid time off.
• Retirement benefits.
• Parental leave.
• Potential relocation assistance, subject to policy.
• Mentorship from expert faculty and support and encouragement to apply for training grants.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: Ph.D. or professional degree