Computational Biologist, AI​/ML

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Biohub is leading the new era of AI-powered biology to cure or prevent disease through its 501c3 medical research organization, with the support of the Chan Zuckerberg Initiative.

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 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.

• 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

Biohub is seeking an accomplished Computational Biologist to join our interdisciplinary team. This role requires experience in research settings, a background in biology, and a proven ability to design, evaluate, and publish innovative computational methodologies that leverage machine learning, statistics, language modelling, and AI to advance biological research and discovery. Research projects to accelerate the rate of scientific discovery will be assigned by the President, and in collaboration with research teams across the organization.

The ideal candidate will have a strong track record of accomplishments and a dedication to collaborative work within a highly interdisciplinary environment.

• Contribute to a dynamic, innovative, and collaborative program that aligns with the mission of Biohub.
• Develop and evaluate cutting‑edge computational methodologies using data generated from across all research groups and incorporating relevant available datasets to develop predictive models.
• Collaborate within an interdisciplinary research environment to develop, test, and validate models.
• Engage with colleagues throughout the Biohub to uphold our values of scholarly excellence, innovation, open communication, hands‑on hacking, and partnership.
• Communicate progress and results with colleagues inside and outside of your team.
• Publish and disseminate impactful findings through preprints (medRxiv, bioRxiv) and/or software repositories (e.g., Git Hub).
• Work with the Biohub team to patent and license technologies resulting from your research.

• PhD in Computational Biology, AI / Machine learning, Applied Statistics or a MS plus relevant job experience.
• Background in relevant areas of biomedical science, demonstrating a deep understanding of cellular biology, transcription and protein signal transduction.
• 2–4 years of post‑doctoral and/or industry experience demonstrating the ability to implement, evaluate, and create new computational methodologies that leverage machine learning, statistics, and AI for biological research and discovery.
• Experience in building and evaluating machine learning and/or neural network models on biological data, with a deep understanding of feature selection, regularization, model introspection, and interpretability.
• Proficiency in using and modifying probabilistic learning or deep learning models such as RNNs, GNNs, protein sequence models, or natural language processing models.
• Proven track record of individual innovation, as well as a strong ability to work collaboratively.
• Outstanding interpersonal and communication skills.
• Demonstrated commitment to open science and alignment with the mission and values of Biohub.

The New York City, NY base pay range for a new hire in this role is Computational Biologist I = $ – $ and Computational Biologist II = $ – $. New hires are typically…