Computational Biologist

About the role

Department

Bsd Med

Genetic Medicine - Im Research Staff

Job Summary

We are seeking a lifelong learner interested in developing statistical and computational tools to sift through large amounts of data to make discoveries that can improve human health.

The Computational Biologist will contribute to the development and application of statistical and computational tools across NIH-funded projects, integrating large-scale GWAS with metabolomics data to uncover mechanisms underlying complex diseases and developing polygenic risk scores that transfer genetic signals across human populations and model organisms to study addiction-related traits, COPD, type 2 diabetes, and other common diseases.

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 from high-dimensional data.
• Develop and train deep learning models to improve upon traditional statistical methods in genomics.
• Collaborate with disease and genomics experts to address pressing biomedical research questions.
• Write and contribute to scientific publications and present findings at lab and consortium meetings.
• Help train and mentor students in the lab.
• A pplies research principles and relevant subject matter knowledge relevant to administer a research project. With a moderate level of direction, manages lab and/or research-related duties and tasks. Helps develop, design and conduct research projects according to plan.
• Takes responsibility for the following laboratory duties: recognizing abnormal results and varying conditions or procedures to correct problems; setting up and performing experiments. Analyze the meaning, significance, causes, and effects of the subject.
• Performs other related work as needed.

Minimum Qualifications

Education

Minimum requirements include a college or university degree in related field.

Work Experience

Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.

Certifications: Preferred Qualifications

Education

• Master's or doctoral degree in computational biology, bioinformatics, statistics, physics, or other quantitative disciplines with an interest in applying skills to biomedical research.

Experience

• Experience with deep learning in genomics and large language models.
• Experience with large datasets, distributed computing, and databases.
• Reproducible research tools and workflows (GitHub, Snakemake, Nextflow).
• Proficiency in R and/or Python.

Preferred Competencies

• Strong Analytical skills.
• Problem-solving skills.
• Organizational skills.
• Verbal and written communication skills.
• Ability to work independently and as part of a team
• Willingness to explore and adopt new ideas and technologies.
• Analytical, statistical, quantitative, and computational/programming skills.

Application Documents

• Resume (required)
• Cover Letter (required)

Additional information

When applying, the document(s) MUST be uploaded via the My Experience page, in the section titled Application Documents of the application.

• Job Family
• Research
• Role Impact
• Individual Contributor
• Scheduled Weekly Hours
• 40
• Drug Test Required
• No
• Health Screen Required
• No
• Motor Vehicle Record Inquiry Required
• No
• Pay Rate Type

Salary

FLSA Status

Exempt

Pay Range $60,000.00 - $80,000.00

The included pay rate or range represents the University's good faith estimate of the possible compensation offer for this role at the time of posting.

Benefits Eligible

Yes

The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in the Benefits Guidebook.

Posting Statement

All offers of employment are contingent upon a background check that includes a review of conviction history. A conviction does not automatically preclude University employment. Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position.

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