Postdoctoral Associate - Computational and Experimental Functional Genomics in Rare Disease

Position overview

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 andtranslate those insights into potential treatments. The position is part of Project Encore, a new initiative funded by an NIH Director's Transformative Research Award (neallab.org/encore).

Encore is developing a scalable approach to therapeutic discovery for rare genetic diseases. We combine barcoded disease-associated variant libraries, multiplexed cell imaging, and in situ barcode sequencing to measure how compounds affect protein localization and cellular organization across hundreds of variants in parallel. The project includes experimental screening of more than 1.6 million compound-variant combinations. Our goal is to identify compounds that reverse disease-associated cellular phenotypes and generate testable therapeutic hypotheses.

This position is for a scientist who wants to personally connect experimental design with computational discovery. You will tackle questions such as: How should a disease phenotype be measured? What counts as a rescue? How can we connect phenotypes and drug responses to identify shared biology between diseases? You will conduct experiments, develop and apply analytical methods, and use the resulting insights to choose and design the next experiments, building a research direction of your own.

Meaningful hands-on experience in both experimental biology and computational analysis is required, although candidates need not have equal depth in both areas or prior experience with every platform-specific method.

Key responsibilities

• Develop and lead research projects. Identify questions that span experiments and analysis, such as which variants share mechanisms or how compounds reverse phenotypes, and develop them into projects you lead.
• Design and perform pooled optical screens.
• Work with barcoded variant libraries, mammalian cell models, multiplexed staining, and in situ sequencing to generate informative single-cell imaging datasets.
• Develop reproducible analysis workflows. Process imaging and barcode data, assess data quality, and generate quantitative cellular profiles using established tools and new approaches where 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. Design and conduct dose-response and disease-relevant validation experiments with experimental and clinical collaborators.
• Share and mentor. Lead manuscript preparation and present findings, contribute open-source software and accessible datasets, and mentor junior researchers.

Required qualifications

• A Ph.D. in computational biology, bioengineering, systems biology, biophysics, cell or 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, with experience independently analyzing biological datasets and implementing well-documented and 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.

Preferred experience

Expertise in all areas is not expected, but candidates with experience in more than one of these areas will be prioritized:

• 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 and career development

• The Neal Laboratory emphasizes interdisciplinary training, scientific independence, and collaborative research. This 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.

• Start date: Flexible; as early as possible
• Location: On-site, Broad Institute, Cambridge, MA

How to apply

• Please submit a CV, contact information for three references, and a brief cover letter. 
• In your cover letter, describe your research interests and your experimental and computational contributions to previous projects, and briefly sketch a question you would want to pursue that bridges the two.
• Applications will be reviewed on a rolling basis until the position is filled.
• We are also recruiting for a complementary position in Rare Disease Mechanisms and Drug Discovery , emphasizing experimental technology development, screening, and mechanistic follow-up in collaboration with computational researchers.