Applied ML Postdoc: Multimodal Cancer AI & Federated Learning

Moffitt Cancer Center; United States; On-site; USD 73,000 - 102,000; Full time

Benefits offered by this job

Medical coverage

Dental insurance

Vision insurance

Paid time off

Retirement benefits

Parental leave

Relocation assistance

Job summary

Moffitt Cancer Center in Tampa, Florida, seeks an Applied Postdoctoral Research Fellow to join Dr. Ghulam Rasool's lab and develop advanced AI methods for multimodal cancer patient modeling.

The work emphasizes federated learning across distributed clinical data environments, with applications in patient-trial matching and outcome prediction. You will build ML models using transformers and GNNs to integrate radiology, pathology, EHR, and molecular data, collaborating with clinicians and

Qualifications

• PhD or equivalent degree in Electrical, Biomedical and/or Computer Engineering/CS/Math/Physics or related discipline is required.
• At least one first-author publication is expected.
• relocation assistance may be provided per policy to ensure a seamless transition.

Responsibilities

• Develop new ML models using Transformers and/or GNNs for multimodal cancer data integration.
• Create pipelines for image, text, and tabular data processing.
• Implement privacy-preserving and federated learning approaches across distributed clinical data environments to support patient-trial matching and outcome prediction.

Skills

NLP & LLMs

Medical imaging

Multimodal ML

EHR data

Federated learning

Privacy-preserving AI

Clinical prediction

Patient-trial matching

Deep learning frameworks

Education

PhD or equivalent

Tools

Transformers

GNNs

TensorFlow

PyTorch

Job description

Moffitt Cancer Center in Tampa, Florida, seeks an Applied Postdoctoral Research Fellow to join Dr. Ghulam Rasool's lab and develop advanced AI methods for multimodal cancer patient modeling.

The work emphasizes federated learning across distributed clinical data environments, with applications in patient-trial matching and outcome prediction. You will build ML models using transformers and GNNs to integrate radiology, pathology, EHR, and molecular data, collaborating with clinicians and