Postdoctoral position in Computational Biology and AI-Enhanced Epigenetics

Postdoctoral position in Computational Biology and AI-Enhanced Epigenetics Description Research Context & Impact

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  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.

• 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 Epigenetics will also be considered.
• 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 Tensor Flow)
• Ability to work autonomously while maintaining regular communication

• Proven skills in Snakemake pipeline development with Conda environments and/or containerization
• Understanding of Expectation-Maximization algorithms or Bayesian statistical methods
• Familiarity with transposable element biology and/or repeat annotation pipelines
• Track record of software tool development and open-source contributions

• 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
• Prepare first-author manuscripts for high-impact journals
• Present research at major conferences
• Coordinate with experimental collaborators for biological validation

Procédure :
Submit application via the UM Career Portal (R):
• CV• Cover Letter
• Names of two references

Providing a link to Github is highly recommended.

Starting date is flexible.