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CORE JOB SUMMARY
The Postdoctoral Associate performs scientific research and studies under the supervision of managing staff. Moreover, the Postdoctoral Associate promotes institutional recognition through literal contributions to the scientific community.
CORE QUALIFICATIONS
Education:
Ph.D. in relevant field
Certification and Licensing:
Not Applicable
Experience:
No prior experience required
Knowledge, Skills and Attitudes:
Skill in collecting, organizing, and analyzing data
Ability to recognize, analyze, and solve a variety of problems
Ability to exercise sound judgment in making critical decisions
Department Specific Functions
This position offers an exciting opportunity to develop cutting-edge AI-enhanced algorithms for analyzing transposable elements (TEs) in cancer epigenomics, with direct translational applications to precision oncology and immunotherapy.
Transposable elements constitute approximately half of mammalian genomes and have emerged as central players in cancer biology, immune regulation, and therapeutic development. Their repetitive nature creates a fundamental computational barrier: sequenced reads cannot be uniquely mapped to specific loci, forcing researchers to discard 5–30% of sequencing data or rely on family-level averages that obscure critical locus-specific regulatory dynamics.
This project addresses these challenges through an integrated computational and biological framework that develops advanced multi-read allocation algorithms leveraging artificial intelligence to achieve locus-level resolution at TEs. The therapeutic relevance is direct: our work will enable rational design of TE-targeted epigenetic interventions, refine TE-based biomarkers for cancer diagnosis and prognosis, and inspire new therapeutic strategies exploiting viral mimicry for cancer immunotherapy. The team actively collaborates with the Sylvester Comprehensive Cancer Center experimental laboratories.
Lead implementation of multi-read allocation algorithms and AI model development
Conduct comprehensive benchmarking across diverse datasets, organisms, and genomic contexts
Develop, document, and release production-quality software packages
Department Specific Qualifications
Education:
PhD in Bioinformatics, Computational Biology, or Computer Science with biological applications. Candidates whose doctoral work focused on deep learning methods and who have a strong interest in genomics will also be considered.
Experience:
At least one publication in computational genomics or machine learning methods
Strong programming skills in Python and/or R
Experience with deep learning frameworks (PyTorch or TensorFlow)
Knowledge, Skills and Attitudes:
Snakemake pipeline development with Conda environments and/or containerization
Strong interest or familiarity with transposable element biology
Ability to understand Expectation-Maximization algorithms or Bayesian statistical methods
Track record of software tool development and open-source contributions would be a plus
The University of Miami offers competitive salaries and a comprehensive benefits package including medical, dental, tuition remission and more.
UHealth-University of Miami Health System, South Florida's only university-based health system, provides leading-edge patient care powered by the ground breaking research and medical education at the Miller School of Medicine. As an academic medical center, we are proud to serve South Florida, Latin America and the Caribbean. Our physicians represent more than 100 specialties and sub-specialties, and have more than one million patient encounters each year. Our tradition of excellence has earned worldwide recognition for outstanding teaching, research and patient care. We're the challenge you've been looking for.
The University of Miami is an Equal Opportunity Employer. Applicants and employees are protected from discrimination based on certain categories protected by Federal law.
Job Status:
Full timeEmployee Type:
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