POSITION SUMMARY
The Ellerby and Gerencser labs at the Buck Institute seek a
Postdoctoral Researcher (PhD required) 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.
PROJECT OVERVIEW
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:
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Translate biological questions into analytical strategies.
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Build and maintain image-analysis pipelines (deep learning and classical methods).
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Work with multiple data modalities, including compound-library databases and phenotypic profiling datasets.
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Support small-molecule screens, cell-painting-based phenotyping, and high-content imaging workflows.
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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.
QUALIFICATIONS
Essential Requirements-
PhD in bioinformatics, computational biology, or a related field
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Experience with biological image processing and automated/pipeline-based image analysis
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Proficiency in Python and common scientific/imaging libraries
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Strong data analysis, database, and biostatistics skills
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Windows and Linux proficiency
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Demonstrated research portfolio or publication record
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Excellent written and oral communication skills
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Strong time-management skills and the ability to work independently and collaboratively
Desired Skills-
Experience with data from high-throughput small-molecule screens
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Experience with laboratory automation and robotic systems
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Microscopic phenotypic profiling or cell-painting experience
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Deep-learning tools (CNNs for object detection, instance segmentation, embeddings)
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GUI development in Python (e.g., Flask, Voila)
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Pathway analysis or knowledge-graph-based approaches
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Programming in R
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Experience with programmatic prompting of large language models
Compensation & Benefits-
Salary Range: $80,000-100,00 per year, depending on experience and qualifications
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Dynamic research environment using state-of-the-art techniques
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Comprehensive benefits package: health insurance, paid parental leave, childcare assistance, generous vacation/sick leave, and a 401(k) with 5% employer match
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Highly collaborative scientific and social community
ABOUT US
The Buck Institute is dedicated to extending human healthspan by addressing the biological mechanisms of aging. As a leading independent non-profit research institute in Marin County, CA, the Buck brings together experts across multiple disciplines to prevent or delay age-related diseases, including Alzheimer’s, Parkinson’s, cancer, cardiovascular disease, and diabetes.
We are committed to fostering an inclusive, welcoming environment. We strongly encourage applicants who will contribute to the diversity of our community.
Core facilities supporting our research include Microscopy (directed by Dr. Akos Gerencser), Bioinformatics, Flow Cytometry, Single Cell Genomics, Proteomics, Metabolomics, and Mouse Phenotyping and Behavior.
TO APPLY
Please submit your CV, a cover letter describing your research interests, and contact information for 2 references.
For Additional Information:
Professor Lisa M. Ellerby, Ph.D., and Research Associate Professor Akos Gerencser MD. Ph.D.
[email protected]
[email protected]
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