Postdoc in Computational Immunology in Yale University – USA

Summary

Yale University School of Medicine is seeking a highly motivated researcher for an Associate Research Scientist or Postdoctoral Associate position in Computational Immunology. The successful candidate will develop computational methods for multiomic immune profiling, with a focus on adaptive immune receptor repertoire sequencing (AIRR-seq). This involves creating statistical machine-learning approaches to integrate various data types, including single-cell RNA-seq and clinical metadata. Potential projects cover genotype-aware repertoire analysis, modeling clonal expansion, and software development for applications in vaccination, infection, and autoimmunity. The role offers opportunities to publish studies, develop open-source software, and collaborate with leading groups. Strong quantitative and programming skills are essential. The application deadline is September 1, 2027.

Key Facts

Department	Pathology, Yale School of Medicine
Position Type	Associate Research Scientist or Postdoctoral Associate
Discipline	Computational Immunology
Research Area	Multiomic immune profiling, adaptive immune receptor repertoire sequencing (AIRR-seq), statistical machine-learning approaches, single-cell RNA-seq, BCR/TCR data, genomics, immune phenotyping, germline variation, genotype-aware repertoire analysis, clonal expansion, receptor sharing, software development, vaccination, infection, autoimmunity, population-scale immune variation
Experience	Experience with AIRR-seq, BCR/TCR repertoire analysis, single-cell genomics, multiomic integration, statistical modeling or machine learning is desirable but not required.
Deadline	2027-09-01
Required Documents	CV, brief statement of research interests, contact information for references
Employer Address	New Haven, CT
Employer Contact	Steven Kleinstein ([email protected]), Gur Yaari ([email protected])

About the Project

The research project focuses on developing computational methods for multiomic immune profiling, with a particular emphasis on adaptive immune receptor repertoire sequencing (AIRR-seq). The successful candidate will develop statistical machine-learning approaches to integrate AIRR-seq data with single-cell RNA-seq, paired BCR/TCR data, genomics, immune phenotyping, germline variation, and clinical or experimental metadata. Potential projects include genotype-aware repertoire analysis, modeling clonal expansion, public and private receptor sharing, scalable software development for applications in vaccination, infection, and autoimmunity, and population-scale immune variation. The project will be jointly mentored by Steven Kleinstein and Gur Yaari.

Key Responsibilities

• Develop computational methods for multiomic immune profiling with emphasis on adaptive immune receptor repertoire sequencing (AIRR-seq)
• Develop statistical machine-learning approaches integrating AIRR-seq with single-cell RNA-seq, paired BCR/TCR data, genomics, immune phenotyping, germline variation, clinical or experimental metadata

Required Skills

• Strong quantitative skills
• Strong programming skills
• Experience with AIRR-seq BCR TCR repertoire analysis (desirable but not required)
• Experience with single-cell genomics (desirable but not required)
• Experience with multiomic integration (desirable but not required)
• Experience with statistical modeling (desirable but not required)
• Experience with machine learning (desirable but not required)

Who Should Apply

Candidates with strong quantitative and programming skills are encouraged to apply. Experience with AIRR-seq, BCR/TCR repertoire analysis, single-cell genomics, multiomic integration, statistical modeling, or machine learning is desirable but not required. Individuals from computational biology, bioinformatics, computer science, statistics, applied mathematics, physics, bioengineering, immunology, or related fields are welcome.

Benefits

• Opportunities to publish methodological biological studies
• Opportunities to develop open-source software
• Opportunities to collaborate with leading immunology and computational biology groups
• Opportunities to contribute to widely used community resources for immune repertoire analysis

About the Employer

Yale University School of Medicine is committed to basing judgments concerning admission, education, and employment upon qualifications and abilities. It seeks to attract qualified persons from a broad range of backgrounds and perspectives. The university does not discriminate in admissions, educational programs, or employment against any individual based on sex, sexual orientation, gender identity/expression, pregnancy, race, color, national/ethnic origin, religion, age, disability, protected veteran status, or other protected classes as set forth by federal and Connecticut law.

Frequently Asked Questions

What is the primary focus of this research position?

The primary focus is to develop computational methods for multiomic immune profiling, with an emphasis on adaptive immune receptor repertoire sequencing (AIRR-seq).

What kind of approaches will the successful candidate develop?

The successful candidate will develop statistical machine-learning approaches for integrating AIRR-seq with single-cell RNA-seq, paired BCR/TCR data, genomics, immune phenotyping, germline variation, and clinical or experimental metadata.

What are the desirable skills for this position?

Experience with AIRR-seq, BCR/TCR repertoire analysis, single-cell genomics, multiomic integration, statistical modeling, or machine learning is desirable but not required.

Which fields are encouraged to apply?

Candidates from computational biology, bioinformatics, computer science, statistics, applied mathematics, physics, bioengineering, immunology, or related fields are encouraged to apply.

What documents are required for the application?

Required documents include a CV, a brief statement of research interests, and contact information for references.

Who are the mentors for this project?

The project will be jointly mentored by Steven Kleinstein and Gur Yaari.