Basile Wicky
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Postdoc: AAV capsid engineering with generative AI ETH Zürich in Switzerland
Degree Level
Postdoc
Field of study
Computer Science
Funding
Available
Country
Switzerland
University
ETH Zürich

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Where to contact
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About this position
The Bio-Engineering Systems for Therapeutics (BEST) postdoc program, part of the Next-gen Bioengineers initiative, offers a unique opportunity for postdoctoral researchers to advance gene therapy through AAV capsid engineering using generative AI. This program is a collaboration between ETH Zürich and Roche Pharma Research and Early Development (pRED), focusing on developing innovative tools and methods to address key challenges in medicine and therapeutic development.
The project centers on Adeno-associated viruses (AAVs), which are the leading platform for gene therapy but face limitations such as sub-optimal tissue tropism, pre-existing human immunity, and low manufacturing yields. The goal is to leverage state-of-the-art generative models in protein design to improve AAV manufacturing properties, thereby enhancing their clinical potential.
As a postdoctoral researcher, you will develop and implement computational workflows for designing novel AAV capsids, lead the computational design process, and participate in wet lab validation of your designs, including library construction, viral production, and NGS-based screening. You will utilize computational structural biology tools to ensure that your designs maintain assembly competence and structural integrity, and process high-throughput experimental data to iteratively refine generative models. The role involves close collaboration at both Roche pRED and ETH Zurich D-BSSE in Basel, translating AI-driven designs into improved AAV vectors.
Applicants must have a PhD or equivalent in computational biology, bioinformatics, bio-ML, or a related quantitative field, with hands-on experience in generative models for proteins. A hybrid mindset, strong motivation to work at the intersection of computational and experimental biology, and excellent communication skills in English are essential. Prior AAV experience is welcome but not required, and candidates should be willing to be trained in experimental validation techniques.
The program offers end-to-end research ownership, allowing you to oversee the entire process from in silico design to experimental validation. You will benefit from a highly collaborative environment between academia and industry, dual supervision by principal investigators from ETH Zurich and Roche, and access to world-class facilities at both campuses in Basel. The fellowship is fully funded for two years, with dedicated support for networking and career development.
ETH Zürich is renowned for its excellence in science and technology, fostering an inclusive culture that values diversity and sustainability. The university promotes equality of opportunity and provides an environment where all staff and students can thrive. Applications are accepted exclusively through the online portal, and the position will be filled on a rolling basis. For further information, contact Prof. Dr. Basile Wicky or Dr. Denis Phichith. Join a global community at ETH Zürich and Roche, working together to develop solutions for today's and tomorrow's challenges in gene therapy and bioengineering.
Funding details
Available
What's required
Applicants must hold a PhD degree or equivalent in computational biology, bioinformatics, bio-ML, or a related quantitative field. Hands-on experience with modern generative models for proteins is required. Candidates should demonstrate a hybrid mindset, with deep interest in applying computational biology to translational challenges; prior AAV experience is welcome but not necessary. High motivation to work at the intersection of wet and dry lab work, willingness to be trained in experimental validation techniques, and excellent oral and written communication skills in English are essential.
How to apply
Submit your online application through the ETH Zurich application portal. Prepare a single PDF containing a letter of motivation (max 1 page), CV (max 2 pages), full list of publications, 3 letters of recommendation, brief statement of research interests (max 1 page), and a copy of your doctoral degree certificate if obtained. Applications via email or postal services will not be considered. The position will be filled on a rolling basis.
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