Scientific Data Analyst/Programmer (Postdoc) - Gerencser lab
About The Position
The Ellerby and Gerencser labs at the Buck Institute seek a PhD-level Postdoctoral Researcher with expertise in microscopy-based image processing, machine vision, and data operations supporting high-throughput small molecule screening. The lab integrates cell biology, advanced microscopy, robotic automation, and single-cell gene expression techniques to investigate cellular function in health and aging. Current efforts focus on defining youthful and aging-related cellular phenotypes to use in high-throughput screening efforts across multiple labs at the Buck Institute. The successful candidate will develop and apply image-analysis pipelines, integrate imaging data with internal and external databases, and contribute to collaborative projects across Buck laboratories. This role involves developing and implementing algorithms, automated workflows, and analytical tools for modern microscopy and high-content imaging. Projects rely heavily on AI-based and classical segmentation methods, object detection, image registration, and object tracking. The lab uses automated microscopes and a fully robotic high-throughput screening system, generating large datasets requiring scalable, automated pipelines. The candidate will: Translate biological questions into analytical strategies. Build and maintain image-analysis pipelines (deep learning and classical methods). Work with multiple data modalities, including compound-library databases and phenotypic profiling datasets. Support small-molecule screens, cell-painting-based phenotyping, and high-content imaging workflows. Contribute to automation of data acquisition and experimental design. Training will be provided in experimental technologies, and the candidate will also participate in experiment execution and data interpretation.
Requirements
- PhD in bioinformatics, computational biology, or a related field
- Experience with biological image processing and automated/pipeline-based image analysis
- Proficiency in Python and common scientific/imaging libraries
- Strong data analysis, database, and biostatistics skills
- Windows and Linux proficiency
- Demonstrated research portfolio or publication record
- Excellent written and oral communication skills
- Strong time-management skills and the ability to work independently and collaboratively
Nice To Haves
- Experience with data from high-throughput small-molecule screens
- Experience with laboratory automation and robotic systems
- Microscopic phenotypic profiling or cell-painting experience
- Deep-learning tools (CNNs for object detection, instance segmentation, embeddings)
- GUI development in Python (e.g., Flask, Voila)
- Pathway analysis or knowledge-graph-based approaches
- Programming in R
- Experience with programmatic prompting of large language models
Responsibilities
- Translate biological questions into analytical strategies.
- Build and maintain image-analysis pipelines (deep learning and classical methods).
- Work with multiple data modalities, including compound-library databases and phenotypic profiling datasets.
- Support small-molecule screens, cell-painting-based phenotyping, and high-content imaging workflows.
- Contribute to automation of data acquisition and experimental design.
Benefits
- health insurance
- paid parental leave
- childcare assistance
- generous vacation/sick leave
- 401(k) with 5% employer match
- Dynamic research environment using state-of-the-art techniques
- Highly collaborative scientific and social community
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
Number of Employees
251-500 employees
