Description

The Hattie Chung Lab at Yale School of Medicine is seeking a motivated postdoctoral researcher to study the cellular and molecular drivers of tissue homeostasis, remodeling, and aging in the ovary. Our systems biology lab integrates in vivo models, single-cell and spatial multi-omics, and machine learning to uncover the mechanisms that maintain tissue function and how they break down with age.

Topic areas for this position include vascular remodeling and clearance in the ovary, immune clearance and inflammaging, and applying functional genomics and in vivo models to uncover mechanistic drivers of age-related dysfunction in the ovary. Applicants should have a strong background in immunology or vascular biology, demonstrated experience with mouse models, and a systems-level perspective on biology. The ideal candidate will excel in a collaborative, interdisciplinary research environment.

This position closes on August 20, 2025, and the successful candidate would start in October or November 2025.

To apply, please email Dr. Hattie Chung with the subject line: “Postdoctoral Candidate – [Your Name]”.

Include the following materials:

• Curriculum Vitae (CV)
• A brief cover letter detailing your interest in our work and directions, your key research accomplishments, scientific interests, career goals
• Contact information for three referencesf

Requirements

Required:

• U.S. citizens or permanent resident (required by the training grant)
• Ph. D. (or equivalent) in Biology, Immunology, Biochemistry, Chemistry, Bioengineering, Systems Biology, or related discipline
• At least one first-author publication in the basic sciences (published or under review).
• Experience with mice models
• Background in immunology, vascular biology, or related
• Experience with flow cytometry and related cell isolation techniques
• Excellent written and verbal communication skills
• Ability to work both independently and collaboratively in a fast-paced environment

Preferred experience:

• Confocal microscopy and live imaging
• Next-generation sequencing (NGS) techniques
• Data science