Multimodal ML Postdoc: Cancer AI & Federated Learning

Moffitt Cancer Center; Tampa (FL); Presencial; USD 73.000 - 102.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Medical coverage

Dental and vision insurance

Paid time off

Relocation assistance

Descripción de la vacante

Moffitt Cancer Center in Tampa, FL invites applications for an Applied Postdoctoral Research Fellow to advance AI methods for multimodal cancer patient modeling. The role emphasizes federated learning across distributed data, with applications in clinical trial matching, treatment outcome prediction, and personalized care.

The ideal candidate has expertise in NLP, large language models, medical imaging, and integrating EHR, pathology, radiology, and molecular data.

Formación

• PhD or equivalent in Electrical, Biomedical and/or Computer Engineering/CS/Math/Physics or related field is required.
• At least one first-author publication.
• Experience with NLP, ML, and healthcare data is preferred.

Responsabilidades

• Develop ML models using Transformers and/or GNNs for multimodal cancer data integration (radiology, pathology, clinical data).
• Develop pipelines for image, text, and tabular data processing for ML models.
• Create privacy-preserving and federated learning approaches across distributed clinical data to support patient-trial matching, treatment outcomes, risk stratification, and personalized care.
• Collaborate with team members on implementing technologies in preclinical/clinical settings, manuscript writing, and grant data preparation.

Conocimientos

NLP

LLMs

Medical imaging

Multimodal ML

EHR data

Clinical text

Radiology data

Pathology data

Molecular data

Federated learning

Privacy-preserving AI

Clinical prediction

Deep learning

Educación

PhD or equivalent

First-author publication

Herramientas

Transformers

GNNs

PyTorch

TensorFlow

Descripción del empleo

Moffitt Cancer Center in Tampa, FL invites applications for an Applied Postdoctoral Research Fellow to advance AI methods for multimodal cancer patient modeling. The role emphasizes federated learning across distributed data, with applications in clinical trial matching, treatment outcome prediction, and personalized care.

The ideal candidate has expertise in NLP, large language models, medical imaging, and integrating EHR, pathology, radiology, and molecular data.