Applied Machine Learning Postdoctoral Fellow

Working at Moffitt is both a career and a mission: to contribute to the prevention and cure of cancer.

As the only National Cancer Institute-designated Comprehensive Cancer Center based in Florida, Moffitt employs some of the best and brightest minds from around the world. Join a dedicated team of nearly 11,000 who are shaping the future we envision. Moffitt has been recognized as a Best and Brightest Company to Work For in the Nation and is continually named one of the Tampa Bay Times’ Top Workplaces.

Summary Dr. Ghulam Rasool seeks to hire a Postdoctoral Fellow to join his lab in the general area of machine learning, artificial intelligence, and molecular data science. Modern cancer research generates large-scale molecular datasets that may include genomic, transcriptomic, proteomic, epigenomic, and other molecular profiling data, as well as associated clinical and outcome information. Extracting meaningful biological and clinical insights from these high-dimensional and heterogeneous datasets remains a fundamental challenge in cancer research.

This project will focus on developing advanced machine learning approaches for molecular representation learning, foundation models, predictive modeling, and biomedical discovery using large-scale molecular and related biomedical datasets. Research activities may involve self-supervised learning, transformer-based architectures, graph-based methods, generative models, and other emerging AI techniques for the analysis of molecular and multimodal biomedical data.

Position Highlights:

The position offers a unique opportunity to work in a world-class research environment with state-of-the-art computational resources and strong collaborations with researchers and clinicians across multiple disciplines.

Opportunities to develop novel machine learning and artificial intelligence methods for analyzing large-scale molecular, clinical, and biomedical datasets.

Opportunity to contribute to cutting-edge research in representation learning, foundation models, multimodal AI, and predictive modeling for cancer research and precision medicine.

Access to diverse biomedical data resources, including molecular profiling data and other clinical and research datasets, enabling the exploration of challenging and impactful scientific problems.

Moffitt integrates outstanding patient care with cutting-edge clinical, translational, and basic science research. Having a comprehensive cancer center and hospital on-site provides unique opportunities for high-impact translational research.

Outstanding mentorship from expert faculty with diverse funded research programs spanning machine learning, computational biology, cancer research, and biomedical data science.

Opportunities to publish in leading scientific venues, collaborate across disciplines, and participate in the development of competitive fellowship and grant applications.

The Ideal Candidate :

Background in Computer Science, Electrical Engineering, Computer Engineering, Mathematics, Statistics, Physics, Data Science, Computational Biology, Bioinformatics, or a related quantitative discipline.

Strong computing, programming, and analytical skills, preferably in Python and modern machine learning frameworks such as PyTorch.

Experience with machine learning, deep learning, artificial intelligence, data mining, statistical modeling, or related computational methods.

Interest in developing and applying advanced AI methods to large-scale molecular, genomic, clinical, or other biomedical datasets.

Familiarity with representation learning, foundation models, transformer architectures, graph-based methods, self-supervised learning, or related machine learning techniques is desirable.

Experience working with cancer, biological, molecular, or other real-world healthcare datasets is required.

Strong written and verbal communication skills, with an interest in interdisciplinary and collaborative research.

Responsibilities:

Develop novel machine learning models for molecular and multi-omics data analysis, representation learning, and predictive modeling.

Develop methods for learning robust patient and molecular representations from large-scale genomic, transcriptomic, and other molecular profiling datasets.

Design and implement scalable pipelines for molecular data integration, feature representation, and downstream machine learning applications.

Investigate and apply advanced AI approaches, including foundation models, self-supervised learning, transformer architectures,…