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Department

BSD IPP - Machine Learning

About the Department

The Institute for Population and Precision Health (IPPH), located in the Biological Sciences Division, will integrate a wide spectrum of factors such as human health behaviors, environmental factors, social and economic factors, policies and genetic determinants of health, into studies focused on the treatment and prevention of disease, as well as the maintenance of wellness. Leveraging and integrating the University of Chicago's considerable institutional strength in population science with research spanning diverse fields such as genetic medicine, cancer epidemiology, microbiome, and epigenomics, the Institute will have the common goal of improving human health outcomes. Another major goal of the Institute will be to develop a new multidisciplinary training program to equip researchers with emerging tools and methods to conduct precision health research within a population health framework. Our faculty lead research projects in Artificial Intelligence, biostatistics, epidemiology, and health services research and participate in interdisciplinary teams with faculty in other departments to address complex problems in health and healthcare, in our communities and around the globe. This at-will position is wholly or partially funded by contractual grant funding, which is renewed under provisions set by the grantor of the contract. Employment will be contingent upon the continued receipt of these grant funds and satisfactory job performance.

Job Summary

The Staff Scientist will conduct original research modeling immune-microbe interactions using computational and machine learning approaches. The role emphasizes exploratory work at the intersection of immunology and AI.
The Staff Scientist will work in a collaborative environment with computational and experimental investigators in the IPPH and at the Laboratory for Computational Immunology, University of Chicago, and partner institutions.
This at-will position is wholly or partially funded by contractual grant funding which is renewed under provisions set by the grantor of the contract. Employment will be contingent upon the continued receipt of these grant funds and satisfactory job performance.

Responsibilities

• Develop and apply computational and machine learning methods to model host immune responses to microbial communities, integrating microbiome and protein-level analyses.

• Pursue independent lines of inquiry at the intersection of microbial proteins, immunology, and AI, generating new hypotheses and innovative research directions.

• Partner with wet-lab teams to connect computational predictions with microbiome and immunological data, facilitating cross-disciplinary insights and translational outcomes.

• Promote open science principles, share code and data, and engage with the scientific community via conferences, seminars, and collaborative initiatives.

• Serves as a resource for collecting data and performing analysis. Facilitates and promotes a research project by providing scientific or intellectual information.

• Creates first drafts for scientific writing and publications, including protocols and grants.

• Performs other related work as needed.

Minimum Qualifications

Education:

Minimum requirements include a PhD in related field.

Work Experience:

Minimum requirements include knowledge and skills developed through 5-7 years of work experience in a related job discipline.

Certifications:

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Preferred Qualifications

Education:

• Ph.D. in Computational Biology, Bioinformatics, Computer Science, or a closely related quantitative field.

Experience:

• At least one year of research experience in machine learning applied to microbiology, protein science or related biological problems.

• Prior experience in one or more of the following areas: Experience at the intersection of microbiology and machine learning. Familiarity with immune-specific datasets (e.g., IEDB, OAS, SAbDab) or structural databases (PDB, UniProt). Track record of publishing in top-tier AI, structural biology, or computational biology venues.

Preferred Competencies

• Demonstrated experience applying computational and machine learning methods to microbiome data and protein sequence or structural data.

• Proficiency with the Python scientific and ML ecosystem and experience on Linux HPC / SLURM clusters or cloud environments.

• Ability to conduct independent research, mentor junior lab members, and communicate results clearly in writing and in presentations.

• Familiarity with current trends in AI and biological data sciences.

• Commitment to ethical research practices, data integrity, and responsible AI principles.

• Outstanding organization, analytic, and communication (oral and written) skills.

• Ability to work independently, as part of a team, and collaboratively, depending on the job needs.

• Attention to detail and problem-solving skills.

Working Conditions

• The work will take place primarily within an office or dry lab.

• Weekend or evening hours may be required to complete project timelines.

Application Documents

• Resume (required)

• Cover Letter (preferred)

The University of Chicago uses AI-assisted tools to streamline and augment some recruitment processes; however, AI is not used to make hiring decisions.
When applying, the document(s) MUSTbe 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

$80,000.00 - $100,000.00

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

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.

The University of Chicago's Annual Security & Fire Safety Report (Report) provides information about University offices and programs that provide safety support, crime and fire statistics, emergency response and communications plans, and other policies and information. The Report can be accessed online at:http://securityreport.uchicago.edu.Paper copies of the Report are available, upon request, from the University of Chicago Police Department, 850 E. 61st Street, Chicago, IL 60637.