Yale Neurocritical Care & Stroke Research Associate- Magid-Bernstein and Kim Lab

Yale Neurology is seeking a highly motivated research associate for a one- to two-year position beginning in Summer 2026. This role is a joint effort across two neurocritical care labs, specifically focused on advancing research related to Subarachnoid Hemorrhage (SAH): the Kim lab and the Magid-Bernstein Lab. We are looking for a successful candidate who will bridge the gap between clinical data management, wet lab experimentation, and computational analysis.

Primary Responsibilities

• Database Management & Clinical Research: Take a lead role in maintaining and updating a specialized SAH database to support ongoing clinical projects. This includes clinical data collection and ensuring the integrity of longitudinal patient records.

Wet Lab & Biological Processing: Assist with the collection and processing of biological samples. This includes applying wet lab techniques to support research in immunology and transcriptomics/proteomics.

• Computational Analysis: Perform preprocessing and computational analysis of research data. Candidates must be facile with data analysis, ideally using R and/or Python.
• Academic Contribution: Participate in grant writing, abstract and manuscript preparation, and present findings at professional conferences.

Clinical Experience The research associate will gain extensive experience within the Neurosciences ICU. You will interact with critically ill patients and work collaboratively with the medical team. Associates are encouraged to attend department conferences, lectures, and clinical rounds. Our program has a strong track record of preparing associates for medical school and future medical careers.

Qualifications

• Education: Both undergraduate and Master’s degree graduates are welcome to apply.
• Required Skills: Highly motivated with a strong academic record and an interest in neurosciences. Candidates must have previous wet lab experience and proficiency in data analysis using R or Python.
• Preferred Experience: Prior experience with biostatistics, machine learning methods, or the processing of biological signals and genomic data is highly valued.

Application Process Applications will be reviewed on a first-come, first-served basis, and strong candidates will be invited to interview.