Research Associate: Transformer ML for Genomics

Duke; Durham (NC); Hybrid; USD 70,000 - 90,000; Full time

Job summary

A prestigious university in North Carolina is seeking an Associate in Research within its Department of Biostatistics and Bioinformatics. The role involves developing advanced machine-learning models for large-scale biological data and collaborating with a diverse team. Candidates should have a Master’s degree in a quantitative field, strong programming skills, and experience with machine-learning frameworks like PyTorch or TensorFlow. This hybrid-eligible position offers a chance to contribute to impactful AI and genomics research.

Qualifications

• Master’s degree in Computer Science, Statistics, Biostatistics, Bioinformatics, Computational Biology, or a related quantitative field.
• Strong programming skills.
• Experience with machine‑learning frameworks such as PyTorch or TensorFlow.
• Experience working with large‑scale datasets and computational workflows.

Responsibilities

• Develop and implement transformer‑based and deep‑learning models for large‑scale biological datasets.
• Process, curate, and analyze single‑cell and multi‑omics datasets, including perturbation data.
• Maintain and optimize computational pipelines and model‑training workflows in high‑performance computing environments.
• Collaborate with the Principal Investigator, postdoctoral fellows, and students to advance research objectives and project milestones.
• Support reproducible research, including workflow documentation, manuscript preparation, and grant‑related deliverables.

Skills

Programming skills

Machine learning frameworks (e.g., PyTorch, TensorFlow)

Data analysis

Computational workflows

Education

Master’s degree in a relevant field

Tools

High-performance computing (HPC)

Job description

A prestigious university in North Carolina is seeking an Associate in Research within its Department of Biostatistics and Bioinformatics. The role involves developing advanced machine-learning models for large-scale biological data and collaborating with a diverse team. Candidates should have a Master’s degree in a quantitative field, strong programming skills, and experience with machine-learning frameworks like PyTorch or TensorFlow. This hybrid-eligible position offers a chance to contribute to impactful AI and genomics research.