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 ...
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 ...
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 ...
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 ...
Spatial Transcriptomics information
What is spatial transcriptomics?
Spatial transcriptomics is an advanced technique that allows scientists to measure gene expression within the spatial context of tissue samples. Unlike traditional RNA sequencing, which loses information about where each gene is expressed, spatial transcriptomics preserves the physical location of gene activity in tissues. This helps researchers better understand how cells function within their native environments and interact with neighboring cells, which is especially valuable in fields like cancer research, neuroscience, and developmental biology. The method combines microscopy, molecular biology, and computational analysis to produce detailed maps of gene expression.
What are some common challenges faced by professionals working in spatial transcriptomics, and how can they be addressed?
Professionals in spatial transcriptomics often encounter challenges related to handling large, complex datasets and integrating spatial information with gene expression data. Ensuring high-quality sample preparation and mastering advanced imaging or sequencing technologies are also frequent hurdles. These challenges can be addressed by collaborating closely with multidisciplinary teams—including bioinformaticians, molecular biologists, and imaging specialists—and staying up-to-date with the latest software tools and protocols. Continuous learning and effective communication within the team are key to overcoming technical and analytical obstacles in this rapidly evolving field.
What are the key skills and qualifications needed to thrive as a Spatial Transcriptomics Scientist, and why are they important?
To thrive as a Spatial Transcriptomics Scientist, you need a strong background in molecular biology, genomics, and bioinformatics, typically supported by an advanced degree in a life science field. Familiarity with spatial transcriptomics platforms (such as 10x Genomics Visium), next-generation sequencing (NGS) technologies, and data analysis tools like R or Python is essential. Strong problem-solving skills, attention to detail, and effective communication are important soft skills for collaborating on interdisciplinary research projects. These skills and qualities are crucial for generating high-quality spatial gene expression data and translating findings into meaningful biological insights.
What are popular job titles related to Spatial Transcriptomics jobs in Michigan? For Spatial Transcriptomics jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Spatial Transcriptomics jobs in Michigan look for? The top searched job categories for Spatial Transcriptomics jobs in Michigan are:
Assistant Scientist / Assistant Professor - Computational Biology
Detroit, MI • On-site
Other
Re-posted 2 days ago
Job description
Henry Ford Health
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.
Required Qualifications
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.
How to Apply:
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: qmi1@hfhs.org
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.
Additional Information
- Organization: Henry Ford Medical Group
- Department: Dermatology - New Center Det
- Shift: Day Job
- Union Code: Not Applicable
About Henry Ford Medical Group
Sourced by ZipRecruiter
Industry
Health care and social assistance
Company size
1 - 10 Employees
Headquarters location
Detroit, MI, US
Year founded
2011