DepartmentBSD IPP - Machine Learning
Job SummaryThe 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.
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 QualificationsEducation: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:---Preferred QualificationsEducation:- 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)
Job FamilyResearch
Role ImpactIndividual Contributor
Scheduled Weekly Hours40
Drug Test RequiredNo
Health Screen RequiredNo
Motor Vehicle Record Inquiry RequiredNo
Pay Rate TypeSalary
FLSA StatusExempt
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 EligibleYes
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.
