Postdoctoral Fellow, AI/ML (BHN - Chicago)

The Team

The Chan Zuckerberg Biohub Network (https://www.czbiohub.org/) is a group of nonprofit research institutes that bring together scientists, engineers, and physicians with the goal of pursuing grand scientific challenges on 10- to 15-year time horizons. The CZ Biohub Network focuses on understanding underlying mechanisms of disease and developing new technologies that will lead to actionable diagnostics and effective therapies.

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 the CZ Biohub Network facilitate research by scientists and clinicians at our home institutions and beyond

Diversity of thought, ideas, and perspectives are at the heart of CZ Biohub Network 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 Network is seeking outstanding early-career scientists to join and participate by continuing their training as a Postdoctoral Fellow. For this particular position, the ideal candidate is expected to have experience in computational structural biology, with a focus on 3D molecular modeling, protein-ligand interactions, and the application of machine learning techniques to predict and analyze molecular structures. The candidate should have a strong quantitative background in computer science, computational chemistry, or biophysics, with demonstrated applying machine learning techniques to structural biology problems.

This role will be based out of the Chicago location.

What You'll Do

• Develop and apply state-of-the-art AI/ML method to model biological sequences and structures.
• Design and implement cutting-edge computational methods for modeling 3D structures of Immune protein complexes.
• In depth analysis of computational 3D biomolecular models.
• Benchmarking structure and sequence based predictive model for downstream tasks.
• Coordinate with our lab scientists to design and optimize design and validation strategies.
• Contribute to scientific publications and presentations at conferences.

What You'll Bring

Essential -

• PhD in Computer Science, Computational Biology, Chemistry or a related quantitative field.
• 2 years in applying deep learning to model biological sequences and structures.
• Strong knowledge of molecular and protein modeling tools (e.g. AlphaFold, Boltz).
• Strong knowledge of biological sequence  foundation models (​​e.g., ESM2, EVO).
• Proficiency in high performance computing systems for large-scale modeling and simulation.
• Experience with PyMol or other molecular visualization software.
• History of writing clean, well-documented, and reproducible code.
• Excellent problem-solving skills and ability to work independently.

Nice to have - 

• Ability to work effectively in teams spanning computational and experimental domains.
• Prior projects focused on modeling 3D biological structures and protein-protein interactions.
• Track record of high-impact publications in computational biology or related fields.
• Familiarity with cheminformatics tools and drug discovery pipelines.

Compensation

The Chicago, IL base pay range for a new hire in this role is $84,150.00. 

Benefits for the Whole You 

We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. 

• Provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Paid time off to volunteer at an organization of your choice. 
• Funding for select family-forming benefits. 
• Relocation support for employees who need assistance moving

If you’re interested in a role but your previous experience doesn’t perfectly align with each qualification in the job description, we still encourage you to apply as you may be the perfect fit for this or another role.

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