Research Associate in Human Immunology - Laura Su's lab
University of Pennsylvania
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Philadelphia, PA
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Not Applicable
Posted April 2, 2026
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Responsibilities
Commitments
Responsibilities
- The successful applicant will have an opportunity to contribute to publication-driven projects aimed at understanding immune dysregulation in autoimmune diseases and identifying novel biomarkers for type 1 diabetes (T1D).
Commitments
This appointment will be initially for one (1) year and continuation during that time period and renewal are based on satisfactory performance and availability of funding (limited to three (3) years).
The candidate must be able to work independently and collaboratively.
Not Met Priorities
What still needs stronger evidence
Requirements
- Expertise is required in the specific area of T cell biology and bioinformatics.
- A proven track record in analyzing bulk and single-cell RNA-seq, TCR-seq, and BCR-seq data is essential.
- Candidates must be proficient in advanced statistical and computational methods and have a solid understanding of adaptive immunity.
- Responsibilities may include performing T cell stimulation, antibody staining, cell sorting, preparing bulk and single-cell sequencing libraries, analyzing and integrating RNA-seq, ATAC-seq, TCR-seq, and BCR-seq multi-omic datasets using R or Python.
- Experience with advanced statistical modeling is expected.
Preferred Skills
- Expertise is required in the specific area of T cell biology and bioinformatics.
- The ideal candidate will bring deep expertise in tissue culture and flow cytometry, and a broad range of immunologic techniques.
- A proven track record in analyzing bulk and single-cell RNA-seq, TCR-seq, and BCR-seq data is essential.
- Candidates must be proficient in advanced statistical and computational methods and have a solid understanding of adaptive immunity.
- Experience with advanced statistical modeling is expected.
- Familiarity with spatial transcriptomics is a plus.
- The successful applicant will have an opportunity to contribute to publication-driven projects aimed at understanding immune dysregulation in autoimmune diseases and identifying novel biomarkers for type 1 diabetes (T1D).
Education
- (Required) – Applicants must have a Ph.D. degree.
The Division of Rheumatology in the Department of Medicine at the Perelman School of Medicine at the University of Pennsylvania seeks candidates for a Research Associate position in the Academic Support Staff. This appointment will be initially for one (1) year and continuation during that time period and renewal are based on satisfactory performance and availability of funding (limited to three (3) years). Expertise is required in the specific area of T cell biology and bioinformatics. The ideal candidate will bring deep expertise in tissue culture and flow cytometry, and a broad range of immunologic techniques. A proven track record in analyzing bulk and single-cell RNA-seq, TCR-seq, and BCR-seq data is essential. Candidates must be proficient in advanced statistical and computational methods and have a solid understanding of adaptive immunity. We are looking for a highly self-motivated individual with strong problem-solving skills, effective communication, and excellent organizational abilities. The candidate must be able to work independently and collaboratively. Applicants must have a Ph.D. degree.
Responsibilities may include performing T cell stimulation, antibody staining, cell sorting, preparing bulk and single-cell sequencing libraries, analyzing and integrating RNA-seq, ATAC-seq, TCR-seq, and BCR-seq multi-omic datasets using R or Python. Experience with advanced statistical modeling is expected. Familiarity with spatial transcriptomics is a plus.
The successful applicant will have an opportunity to contribute to publication-driven projects aimed at understanding immune dysregulation in autoimmune diseases and identifying novel biomarkers for type 1 diabetes (T1D). This position offers significant independence, leadership opportunities on high-impact projects, mentorship of junior lab members, and active involvement in cutting-edge translational immunology research. The role includes strong support for authorship and professional development to foster research independence within a highly collaborative and intellectually stimulating environment.
Equal Employment Opportunity Statement
The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state, or local law.
Responsibilities may include performing T cell stimulation, antibody staining, cell sorting, preparing bulk and single-cell sequencing libraries, analyzing and integrating RNA-seq, ATAC-seq, TCR-seq, and BCR-seq multi-omic datasets using R or Python. Experience with advanced statistical modeling is expected. Familiarity with spatial transcriptomics is a plus.
The successful applicant will have an opportunity to contribute to publication-driven projects aimed at understanding immune dysregulation in autoimmune diseases and identifying novel biomarkers for type 1 diabetes (T1D). This position offers significant independence, leadership opportunities on high-impact projects, mentorship of junior lab members, and active involvement in cutting-edge translational immunology research. The role includes strong support for authorship and professional development to foster research independence within a highly collaborative and intellectually stimulating environment.
Equal Employment Opportunity Statement
The University of Pennsylvania is an equal opportunity employer. Candidates are considered for employment without regard to race, color, sex, sexual orientation, religion, creed, national origin (including shared ancestry or ethnic characteristics), citizenship status, age, disability, veteran status or any class protected under applicable federal, state, or local law.