Postdoctoral Associate (Computational Biology)

Location: On-site at the hirify.global in Cambridge, MA, United States

Salary: $70,000.00–$92,666.67 per year

Company

hirify.global is a biomedical research institute conducting interdisciplinary research into genomic mechanisms of disease and therapeutic discovery.

What you will do

• Develop and lead research projects connecting experimental biology, quantitative imaging, and computational discovery for rare genetic diseases.
• Design and perform pooled optical screens using barcoded variant libraries, mammalian cell models, multiplexed staining, and in situ sequencing.
• Build reproducible workflows to process imaging and barcode data, assess quality, and generate quantitative cellular profiles.
• Evaluate phenotypes and compound rescue using statistical and machine learning approaches, including comparisons of conventional image features and learned representations.
• Connect computational predictions to dose-response and disease-relevant validation experiments with experimental and clinical collaborators.
• Lead manuscripts and presentations, contribute open-source software and accessible datasets, and mentor junior researchers.

Requirements

• Ph.D. in computational biology, bioengineering, systems biology, biophysics, cell or molecular biology, or a related field.
• Substantial hands-on experience with mammalian cell culture, molecular biology, and fluorescence microscopy.
• Proficiency in Python and/or R, with independent experience analyzing biological datasets and implementing well-documented, reproducible analytical workflows.
• Ability to connect computational findings with biological questions, communicate across disciplines, troubleshoot research problems, and advance an independent research project, demonstrated through first-author publications or preprints.

Nice to have

• Experience in high-content image analysis, quantitative cell profiling, or computer vision using tools such as CellProfiler or Pycytominer.
• Experience with statistical modeling or machine learning for biological data, including supervised classification, deep learning, or self-supervised representation learning.
• Experience with pooled genetic screens, lentiviral transduction, barcoded libraries, multiplexed imaging, or in situ sequencing.
• Experience with cloud or high-performance computing environments for scalable imaging-dataset analysis.

Culture & Benefits

• Interdisciplinary training, scientific independence, and collaborative research in the Neal Laboratory.
• Mentorship supporting academic and biotechnology career paths.
• Support for applying for external fellowships.
• Medical, dental, vision, life, and disability insurance, plus a 401(k) retirement plan.
• Flexible spending and health savings accounts, paid holidays, winter closure, paid time off, parental and family care leave, and an employee assistance program.

Hiring process

• Submit a CV, contact information for three references, and a brief cover letter.
• Describe research interests, experimental and computational contributions, and a proposed question bridging both areas in the cover letter.
• Applications are reviewed on a rolling basis until the position is filled; the start date is flexible and may be as early as possible.