
Computational Biologist
Job Description
Department
BSD MED - Genetic Medicine - Im Research Staff
About the Department
The Section of Genetic Medicine conducts impactful investigations focused on quantitative genetics, systems biology and genomics, bioinformatics and computational biology as well as through extensive collaborations of the faculty throughout the Department of Medicine, Biological Sciences Division and the University of Chicago.
Job Summary
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.
Applies 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:
Certifications:
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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)
When applying, the document(s) MUST be uploaded via the My Experience page, in the section titled Application Documents of the application.
Job Family
Role Impact
Scheduled Weekly Hours
Drug Test Required
Health Screen Required
Motor Vehicle Record Inquiry Required
Pay Rate Type
FLSA Status
Pay Range
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
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
The University of Chicago is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, or expression, national or ethnic origin, shared ancestry, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination.
Job seekers in need of a reasonable accommodation to complete the application process should call 773-702-5800 or submit a request via Applicant Inquiry Form.
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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