Computational Biologist: Genomics & Deep Learning

The University of Chicago; Chicago (IL); On-site; USD 60,000 - 80,000; Full time

Job summary

The University of Chicago in Chicago, IL, seeks a Computational Biologist to develop statistical and computational tools for large genomic datasets, integrating GWAS with metabolomics to uncover disease mechanisms and build polygenic risk scores across populations.

You will work with RNA-seq, WGS, and single-cell data, train deep learning models, collaborate with genomics experts, publish findings, and mentor students in a collaborative lab environment.

Qualifications

• Minimum qualifications include a college or university degree in a related field.
• 2-5 years of work experience in a related job discipline.
• Master's or doctoral degree in computational biology, bioinformatics, statistics, physics, or other quantitative disciplines.

Responsibilities

• Analyze large-scale genomic datasets including RNA-seq, whole genome sequence, and single-cell data.
• Develop and apply statistical and computational methods to extract biological insights.
• Develop and train deep learning models to improve methods in genomics.
• Collaborate with disease and genomics experts on biomedical research questions.
• Write and contribute to publications and present findings at lab meetings.
• Mentor students in the lab.
• Manage lab and research duties with moderate direction.
• Set up experiments and analyze results to interpret significance.

Skills

Analytical skills

Problem-solving

Organizational skills

Verbal and written communication

Independent work & teamwork

Adaptability

Analytical, statistical, quantitative,

Education

Master's or doctoral degree in computational biology, bioinformatics, statistics, physics, or other quantitative disciplines

Tools

R

Python

GitHub

Snakemake

Nextflow

Job description

The University of Chicago in Chicago, IL, seeks a Computational Biologist to develop statistical and computational tools for large genomic datasets, integrating GWAS with metabolomics to uncover disease mechanisms and build polygenic risk scores across populations.

You will work with RNA-seq, WGS, and single-cell data, train deep learning models, collaborate with genomics experts, publish findings, and mentor students in a collaborative lab environment.