Senior Staff Associate I - Endocrinology

Position Description

Dr. Joanna Spencer‑Segal is seeking a highly motivated candidate for a full‑time Senior Staff Associate I position at Columbia University Irving Medical Center. The Spencer‑Segal Laboratory investigates the neural circuits that regulate emotional behaviors—both innate and stress‑induced—and how these circuits interact with the neuroendocrine stress response. The lab studies how stress hormones act on brain regions involved in mood and behavior, with the goal of defining the pathways that drive stressor‑specific neuroendocrine and behavioral outcomes.

A second major research focus examines the neuroendocrinology of critical illness and sepsis. Critical illness carries high mortality, and survivors frequently experience long‑term mental health challenges, including anxiety, depression, PTSD, and cognitive dysfunction. The lab aims to understand how the neuroendocrine stress response shapes both short‑ and long‑term outcomes—such as the influence of circulating hormones on memory formation during illness, the role of inflammatory mediators in emotional states, and mechanisms underlying glucocorticoid resistance.

The Senior Staff Associate I will work closely with Dr. Spencer‑Segal as the laboratory’s computational and technical lead. They will apply advanced computational approaches to large‑scale neural activity, behavioral, and molecular datasets; build and maintain the lab’s computational infrastructure; and develop the lab’s pipeline for longitudinal one‑photon calcium imaging analysis. They will lead independent research projects from conceptualization through experimentation, analysis, visualization, and manuscript preparation, while also supporting the computational needs of lab members and collaborators.

Responsibilities

• Utilize and manage HPC and cloud environments for large‑scale data processing, model training, and workflow optimization.
• Lead analysis of synchronized neural, behavioral, and biological datasets, including customization and extension of pipelines for in vivo calcium imaging.
• Apply advanced approaches such as clustering, classification, dimensionality reduction, and pattern discovery.
• Implement statistical and machine learning models using scikit‑learn, PyTorch, TensorFlow, and OpenCV.
• Perform bulk RNA‑seq and other high‑dimensional molecular analyses, integrating molecular data with neural and behavioral datasets.
• Build and thoroughly document reproducible analysis pipelines in Python and R.
• Develop interactive dashboards and visualization tools to support data exploration and interpretation.
• Provide expertise on statistical planning, experimental design, and quality‑control strategies.
• Lead independent research projects and contribute to collaborative scientific initiatives.
• Train, mentor, and supervise undergraduate and junior researchers in computational methods.
• Assist the PI and lab personnel in preparing abstracts, manuscripts, and grant applications.
• Perform all other activities necessary to support the research program of Dr. Spencer‑Segal.

Qualifications:

Minimum Qualifications: 

• Bachelor’s degree in Computer Science and six years of relevant experience.

Preferred Qualifications: 

• Master's degree in Computer Science 
• Demonstrated history of first‑author and collaborative co‑authored papers.
• Extensive experience with Python, R, and statistical/machine‑learning methods, including clustering, classification, dimensionality reduction, and predictive modeling. 
• Proficiency with modern machine‑learning frameworks.
• Experience with HPC/cloud environments, reproducible pipelines, and large‑scale multimodal data analysis.
• Experience integrating experimental, behavioral, and bioinformatics datasets.
• Strong mentoring, leadership, communication, and scientific‑writing skills.
• Experience with in vivo calcium imaging and behavioral neuroscience is highly desirable.

Salary Range : $85,000 - $95,000

Application Instructions: apply.interfolio.com/187560

Pay Transparency Disclosure : The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training.  The above hiring range represents the University’s good faith and reasonable estimate of the range of possible compensation at the time of posting.