Staff Scientist

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 cl