Postdoc: AAV capsid engineering generative AI

The highly competitive Bio-Engineering Systems for Therapeutics (BEST) postdoc program, part of the Next-gen Bioengineers initiative,is run jointly by ETH Zurich and Roche Pharma Research and Early Development (pRED). The program focuses on developing new tools and methods to address challenges in medicine and therapeutic development.

Adeno-associated viruses (AAVs) are the leading platform for gene therapy, yet their clinical potential is often capped by sub-optimal tissue tropism, pre-existing human immunity, and low manufacturing yields. This project aims to develop and deploy state-of-the-art generative models in protein design for improving AAV manufacturing properties.

• Generative modeling: develop and implement state-of-the-art computational workflows to design novel AAV capsids
• Dry-to-wet: lead the computational design process and actively participate in wet lab validation of your designs (e.g., library construction, viral production, NGS-based screening)
• Structural analysis: utilize computational structural biology tools to ensure designs maintain assembly competence and structural integrity
• Data integration: process and analyze high-throughput experimental data to iteratively refine generative models
• Collaboration:
  
  work at Roche pRED, and ETH Zurich D-BSSE (both located in Basel) to translate AI-driven designs into improved AAV vectors

• PhD degree or equivalent in computational biology, bioinformatics, bio-ML, or a related quantitative field (required)
• Hybrid mindset: you are deeply interested in applying computational biology to solving translational challenges; AAV experience is welcome but not necessary
• Generative AI: hands‑on experience with modern generative models for proteins (required)
• Interdisciplinary drive: high motivation to work at the intersection between wet and dry lab work, with a willingness to be trained in experimental validation techniques
• Communication: excellent oral and written communication skills in English, with the ability to bridge the gap between ML concepts and experimental molecular biology

• End‑to‑end research ownership: the unique opportunity to own the entire process; from in silico design to experimental validation
• Innovation: an exciting and highly collaborative research environment between academia (ETH) and industry (Roche)
• Mentorship: dual supervision by ETH Zurich and Roche principal investigators
• Infrastructure: full access to world‑class facilities at both the Roche and ETH Zurich campuses in Basel
• Development: a fully funded 2‑year fellowship including dedicated support for networking and career development

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