Research Associate, Synthetic Biology
BioHubNew York, NY (Onsite)$71k – $89kPosted 18 April 2026
Job Description
Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere.
The Team
The Biohub in New York is an independent nonprofit research institute that brings together three powerhouse universities - Columbia University, The Rockefeller University, and Yale University - into a single collaborative technology and discovery engine. Biohub itself supports some of the brightest, boldest engineers, data scientists, and biomedical researchers to investigate the fundamental mechanisms underlying disease and develop new technologies that will lead to actionable diagnostics and effective therapies. We are guided by our values of scholarly excellence; disruptive innovation; hands-on engineering/hacking/building; partnership and collaboration; open communication and respect; inclusiveness; and opportunity for all.
Our Vision
We pursue large scientific challenges that cannot be pursued in conventional environments
We enable individual investigators to pursue their riskiest and most innovative ideas
The technologies developed at Biohub facilitate research by scientists and clinicians at our home institutions and beyond
Diversity of thought, ideas, and perspectives are at the heart of Biohub and enable disruptive innovation and scholarly excellence. We are committed to cultivating an organization where all colleagues feel inspired and know their work makes an important contribution.
The Opportunity
The Biohub is seeking a skilled and motivated Research Associate to support innovative investigations into spatial architecture of molecular systems and its role in controlling cell state dynamics. In our newly established Synthetic Spatial Omics Laboratory , we advance next-generation spatial multi-omics platforms for understanding how cells process combinatorial signals, designing synthetic biology toolkits, and capturing cellular temporal histories with spatial resolution.
The successful candidate will contribute to projects integrating spatial multi-omics and synthetic biology to investigate molecular neighborhood reorganization in response to perturbations, engineer programmable cellular functions, and identify the molecular drivers of cell state transitions. This position offers the opportunity to develop expertise at the intersection of technology development and fundamental discovery in a highly collaborative, interdisciplinary research environment.
What You'll Do
Perform imaging-based multi-omics experiments (e.g., seqFISH and related approaches) to map subcellular molecular organization and cell state transitions.
Carry out perturbation experiments to decode how molecular neighborhoods respond to combinatorial signals.
Assist in engineering and characterizing synthetic biology systems using spatial multi-omics approaches.
Support data processing and analysis using established computational workflows.
Collaborate with team members across experimental and computational disciplines.
Contribute to preprints, publications, presentations, and open science practices as part of the research team.
What You'll Bring
Bachelor’s or Master’s degree in Molecular Biology, Cell Biology, Biochemistry, Bioengineering or a closely related discipline.
1-2+ years of hands-on research experience in an academic and/or industry laboratory setting.
Solid foundation in molecular biology techniques, including cell culture, nucleic acid workflows, and cellular functional assays.
Working proficiency in Python, R, or comparable languages for the analysis of imaging and sequencing data.
Strong organizational skills, attention to detail, and com ... (truncated, view full listing at source)
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