Assistant Scientist / Assistant Professor - Computational Biology

Henry Ford Health (HFH) in Detroit, Michigan, is one of the nation’s leading comprehensive health systems, recognized for excellence in clinical care, research, and education. The Center for Cutaneous Biology and Immunology (CCBI) is a dynamic, multidisciplinary research program dedicated to advancing our understanding of skin biology and immunology, cancer immunology, and the functional genomics that govern immune cell behavior in cancer as well as autoimmune and inflammatory diseases. Our team fosters an innovative, collaborative, diverse, and open-minded research environment in partnership with Michigan State University. We are supported by multiple NIH-funded grants and active communities of immunologists, molecular biologists, biochemists, data scientists, physician scientists, and computational biologists. Our mission is to advance translational research that leads to meaningful improvements in clinical care.

Position Description

We invite applications for an Assistant Professor / Assistant Scientist with expertise in computational biology, statistical genetics, genomics, and AI-driven medicine. We seek a highly motivated individual who develops and applies state-of-the-art computational methods to complex, large-scale biological datasets. The successful candidate will contribute to high-impact translational research programs and lead independent research efforts, and will hold a joint faculty appointment (Assistant Scientist) with Michigan State University as part of the HFH–MSU Health Sciences partnership.

Key Responsibilities

• Develop and apply computational, statistical, and AI/ML approaches to analyze diverse biological datasets, including:
• GWAS, whole-genome/exome sequencing
• DNA methylation and epigenomic profiling
• Bulk and single-cell RNA-seq, spatial transcriptomics
• ATAC-seq (bulk and single-cell), proteomics, CyTOF, and IMC
• Histological and radiological imaging data
• Clinical and epidemiological datasets
• Lead independent research projects and contribute to collaborative team science initiatives.
• Pursue external funding (e.g., NIH, NSF, foundations) to support research programs.
• Mentor trainees and collaborate closely with investigators across HFH and Michigan State University.
  

• PhD in biostatistics, bioinformatics, computational biology, computer science, or a related discipline.
• Strong research track record in genetics, multi-omics integration, and/or AI applications to biological or clinical data, as demonstrated by peer-reviewed publications and conference presentations.
• Demonstrated ability—or strong potential—to secure external research funding.
• Proficiency in programming and analytical languages/platforms (e.g., R, Python, TensorFlow, PyTorch).
• Experience working in Unix/Linux environments, including shell scripting (Bash, awk, sed).
• Familiarity with tools for genomic, epigenomic, transcriptomic, and proteomic analysis, including next-generation sequencing pipelines (DNA-seq, RNA-seq, ATAC-seq, ChIP-seq).
• Experience with single-cell and spatial transcriptomics, eQTL/pQTL analysis, and multimodal data integration.
• Familiarity with imaging analytics (e.g., spatial transcriptomics, H&E, IMC, radiological imaging).
• Experience in human subjects research, healthcare data, epidemiology, or biomedical applications.
• Excellent communication, interpersonal, organizational, and collaborative skills, with the ability to work effectively with colleagues of diverse technical and scientific backgrounds.

Please submit your CV, cover letter, and research statement (past accomplishments, current work, and future research vision) to:

Dr. Qing-Sheng Mi, MD, PhD

Director, Center for Cutaneous Biology and Immunology (CCBI)

Email: [email protected]

Equal Employment Opportunity/Affirmative Action Employer

Henry Ford Health is committed to the fair and equitable treatment of all individuals and prohibits discrimination based on race, color, creed, religion, age, sex, national origin, disability, veteran status, marital or family status, gender identity, sexual orientation, height, weight, genetic information, or any other protected category in accordance with federal and state laws.