Postdoctoral Associate - Human Genetics

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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 JOB FUNCTIONS
1. Conducts research on specific areas of study as assigned.
2. Prepares research reports and technical papers for publishing.
3. Investigates the feasibility of applying a wide variety of scientific principles and theories.
4. Maintains substantial knowledge of state-of-the-art principles and theories.
5. Develops advanced analytical models and systems and provides solutions and analyses to support strategic and tactical decisions.
6. Adheres to University and unit-level policies and procedures and safeguards University assets.

This list of duties and responsibilities is not intended to be all-inclusive and may be expanded to include other duties or responsibilities as necessary.
 

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