Postdoctoral Associate - Neal Laboratory

Broad Institute • Cambridge, MA

5d • $70,000 - $92,667 • 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 for Project Encore, an NIH Director's Transformative Research Award-funded initiative. The project combines experimental biology, quantitative imaging, and computational analysis to uncover mechanisms of rare genetic diseases and translate those findings into potential treatments.

Project Encore aims to develop a scalable therapeutic discovery approach using barcoded disease-associated variant libraries, multiplexed cell imaging, and in situ barcode sequencing. These methods measure compound effects on protein localization and cellular organization across hundreds of variants in parallel. The project encompasses experimental screening of more than 1.6 million compound-variant combinations, seeking compounds that reverse disease-associated cellular phenotypes and generate testable therapeutic hypotheses.

The role connects experimental design with computational discovery. The Postdoctoral Associate will conduct experiments, develop and apply analytical methods, and use findings to inform subsequent experiments while building an independent research direction. Meaningful hands-on experience in both experimental biology and computational analysis is required; equal depth in both areas or prior experience with every platform-specific method is not required.

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

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 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 following areas is prioritized, though expertise in all 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.

Responsibilities

• Develop and lead research projects that connect experimental questions and analysis, including studies of shared variant mechanisms and compound effects on phenotypes.
• Design and perform pooled optical screens using barcoded variant libraries, mammalian cell models, multiplexed staining, and in situ sequencing.
• Process imaging and barcode data, assess data quality, and create quantitative cellular profiles using established tools or new approaches.
• 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.
• Design and conduct dose-response and disease-relevant validation experiments with experimental and clinical collaborators, guided by computational predictions.
• Lead manuscript preparation, present findings, contribute open-source software and accessible datasets, and 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 for applying for external fellowships

What This Job Offers

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