Lead Computational Immunology Scientist

UT Southwestern Medical Center; Texas; On-site; USD 120,000 - 190,000; Full time

Job summary

UT Southwestern Medical Center in Dallas seeks a Computational Research Scientist to lead machine-learning efforts that identify novel immunotherapy targets and guide the engineering of next-generation cancer therapies. You will build integrative models from multi-omic data and collaborate with wet-lab and clinical teams to translate findings to the clinic.

We value deep expertise in Python (and R), modern ML frameworks, and a strong publication record.

Qualifications

• Ph.D. in Bioinformatics, Computational Biology, Biomedical Engineering, or related quantitative field.
• Strong record of independent research and first-author publications applying ML to biological data.
• Experience with Python, ML frameworks, and large omics datasets.

Responsibilities

• Build predictive models that integrate genomic, transcriptomic, epigenomic, single-cell, and functional data to nominate novel targets in cancer and the tumor microenvironment.
• Drive immunotherapy engineering by applying ML to antigen/neoantigen discovery, de novo protein design, and engineered cell optimization.
• Develop methods for interpreting high-dimensional data, including deep learning for sequence, structure, and single-cell readouts.
• Partner with wet-lab scientists to design experiments, prioritize candidates, and iterate between computation and bench work.
• Lead and mentor trainees, set analytical standards, and help shape the program's scientific roadmap.
• Communicate results through high-impact publications and collaborations across UT Southwestern.

Skills

Python

R

Machine learning

Genomics

Single-cell

Neoantigen discovery

Education

PhD in Computational Biology

Tools

TensorFlow

PyTorch

R (stats)

Job description

UT Southwestern Medical Center in Dallas seeks a Computational Research Scientist to lead machine-learning efforts that identify novel immunotherapy targets and guide the engineering of next-generation cancer therapies. You will build integrative models from multi-omic data and collaborate with wet-lab and clinical teams to translate findings to the clinic.

We value deep expertise in Python (and R), modern ML frameworks, and a strong publication record.