Postdoctoral Associate - Neal Laboratory

Broad Institute • Cambridge, MA

3d • Onsite

About The Position

The Neal Laboratory within the Broad Institute's Novo Nordisk Foundation Center for Genomic Mechanisms of Disease seeks an independent Postdoctoral Associate to integrate experimental biology, quantitative imaging, and computational analysis to uncover mechanisms of rare genetic diseases and translate those insights into potential treatments. The role is part of Project Encore, an initiative funded by an NIH Director's Transformative Research Award that is developing a scalable approach to therapeutic discovery for rare genetic diseases. The project combines barcoded disease-associated variant libraries, multiplexed cell imaging, and in situ barcode sequencing to measure compound effects on protein localization and cellular organization across hundreds of variants in parallel, with more than 1.6 million compound-variant combinations to screen.

The scientist will connect experimental design with computational discovery by conducting experiments, developing and applying analytical methods, and using the resulting insights to guide subsequent experiments and build an independent research direction. Meaningful hands-on experience in both experimental biology and computational analysis is required, though equal depth in both areas and prior experience with every platform-specific method are not required.

The Neal Laboratory emphasizes interdisciplinary training, scientific independence, and collaborative research. The position offers opportunities to develop expertise connecting experimental and computational approaches, with mentorship supporting academic and biotechnology career paths. Postdoctoral researchers are encouraged and supported in applying for external fellowships. The start date is flexible and may be as early as possible. The position is on-site at the Broad Institute in Cambridge, Massachusetts. Applications are reviewed on a rolling basis until the position is filled.

Requirements

• Ph.D. in computational biology, bioengineering, systems biology, biophysics, cell biology, molecular biology, or a related field.
• Substantial hands-on experimental experience, including mammalian cell culture, molecular biology, and fluorescence microscopy.
• Proficiency in Python and/or R.
• Experience independently analyzing biological datasets and implementing well-documented, reproducible analytical workflows.
• Demonstrated ability to connect computational findings with biological questions, communicate clearly across disciplines, troubleshoot research problems, and advance a research project independently, as shown by first-author publications or preprints.

Nice To Haves

• Experience in more than one of the preferred areas is prioritized, though expertise in all areas is not expected.
• High-content image analysis, quantitative cell profiling, or computer vision, using tools such as CellProfiler or Pycytominer.
• Statistical modeling or machine learning for biological data, including supervised classification, deep learning, or self-supervised representation learning.
• Pooled genetic screens, lentiviral transduction, barcoded libraries, multiplexed imaging, or in situ sequencing.
• Cloud or high-performance computing environments for scalable analysis of imaging datasets.
• Mentorship experience.

Responsibilities

• Develop and lead research projects that connect experimental questions and computational analysis, including investigating shared mechanisms among variants and how compounds reverse disease-associated phenotypes.
• Design and perform pooled optical screens using barcoded variant libraries, mammalian cell models, multiplexed staining, and in situ sequencing to generate single-cell imaging datasets.
• Develop reproducible analysis workflows to process imaging and barcode data, assess data quality, and generate quantitative cellular profiles using established tools or new approaches as needed.
• Evaluate methods for detecting phenotypes and rescue.
• Apply statistical and machine learning approaches to distinguish variant-associated phenotypes, quantify compound responses, and compare conventional image features with learned representations.
• Connect computational predictions to experiments by designing and conducting dose-response and disease-relevant validation experiments with experimental and clinical collaborators.
• Lead manuscript preparation and present findings.
• Contribute open-source software and accessible datasets.
• Mentor junior researchers.

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• Life insurance
• Disability insurance
• 401(k) retirement plan
• Flexible spending and health savings accounts
• At least 13 paid holidays
• Winter closure
• Paid time off
• Parental and family care leave
• Employee assistance program
• Support and encouragement for applying for external fellowships

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree