Doctoral Student in High-throughput Analysis of Protein-Protein Interactions 100%

ETH Zürich; Basel; Vor Ort; CHF 60.000 - 70.000; Vollzeit

Zusammenfassung

ETH Zurich's Nash Lab (affiliated with the University of Basel and ETH Zurich) seeks a Doctoral Student to develop high-throughput, machine-learning–assisted studies of force-dependent protein behavior. You will bridge protein engineering, biophysics, and computational biology, contributing to datasets and predictive models.

You will undergo the D-BSSE Doctoral program in Basel and collaborate with two institutions, with data-to-publication responsibilities and potential teaching involvement.

Qualifikationen

• Master's degree in a relevant field is required.
• Strong quantitative background and interest in protein engineering, mechanobiology, and high-throughput methods.
• Hands-on molecular biology and protein expression experience; yeast display/directed evolution desirable.
• Comfort with large sequencing datasets and Python; ML experience a plus.

Aufgaben

• Establish and validate a high-throughput assay for mechanical stability of protein-protein interactions.
• Cross-validate assay results with AFM measurements.
• Design DNA variant libraries and display on yeast.
• Apply next-generation sequencing to quantify phenotypes.
• Develop computational pipelines to convert sequencing data into fitness values.
• Collaborate with computational biology to develop ML models.
• Prepare data and manuscripts for publication and present results.
• Enroll in and complete the D-BSSE Doctoral program in Basel.
• Contribute to undergraduate teaching and chemistry practicals.

Kenntnisse

Fluent English

Quantitative background

Molecular biology

Protein engineering

Python / programming

Machine learning

Team collaboration

Ausbildung

Master's degree in bioengineering / biochemistry / molecular biology / biophysics

Tools

AFM

Next-generation sequencing

Yeast display

Jobbeschreibung

The Nash Lab (Lab for Engineering Synthetic Systems) is jointly affiliated with the Department of Chemistry at the University of Basel and the Department of Biosystems Science and Engineering of ETH Zurich (located in Basel). We work at the interface of protein engineering, molecular biophysics, and synthetic biology, developing new experimental and computational approaches to understand and engineer proteins for useful biomedical and industrial applications. Our research combines high-throughput screening, directed evolution, and single-molecule biophysical methods, and spans projects from fundamental protein science to translational applications.

Project background

Many proteins in the human body experience shear stress and other types of mechanical force, and this is believed to play a central role in their activity. Yet we still lack high-throughput techniques to measure how proteins respond to mechanical force. This project will develop high-throughput methods involving molecular surface display and deep sequencing to study force-dependent behavior in protein systems. These datasets will, in turn, be used to train machine learning models capable of predicting mechanical behaviors of molecular and cellular systems. As a proof of concept, we will apply this platform to study interactions central to blood clotting, with direct relevance to Von Willebrand disease, a common bleeding disorder. The Doctoral Student will work at the interface of protein engineering, single-molecule biophysics, and machine learning, in close collaboration with a computational biology group (Beerenwinkel) at D-BSSE. The project offers hands‑on training across the full pipeline, from high-throughput experimental design to predictive modeling, and the resulting methods and datasets are intended to serve as an open resource for the broader research community.

Job description

• Establish and validate a quantitative, high-throughput assay for measuring the mechanical stability of a protein-protein interaction relevant to blood clotting
• Cross-validate high-throughput assay results against AFM measurements
• Design and construct DNA variant libraries and display them on yeast
• Apply next-generation DNA sequencing to quantify phenotypes
• Develop computational pipelines to convert sequencing data into quantitative fitness values
• Collaborate with a computational biology group at D-BSSE to develop and benchmark machine learning models
• Prepare data and manuscripts for publication and present results at lab meetings and conferences
• Enroll in and complete the requirements of the D-BSSE Doctoral program in Basel
• Contribute to undergraduate teaching and practical supervision at the University of Basel

Profile

• A Master's degree in bioengineering, chemical engineering, biochemistry, molecular biology, biophysics, or a related field
• A strong quantitative background and genuine interest in protein engineering, mechanobiology, and/or high-throughput experimental methods
• Hands‑on experience with molecular biology and protein expression techniques; prior exposure to yeast display, directed evolution, or single-molecule biophysics (e.g., AFM) is an advantage but not required
• Comfort with, or willingness to learn, computational analysis of large sequencing datasets (Python or equivalent); experience with machine learning is a plusIndependent, careful experimental work combined with the ability to collaborate across two research groups and two institutions
• Willingness to contribute to bachelor level teaching / chemistry practicals
• Fluent written and spoken English
• Eligibility for enrollment in the D-BSSE Doctoral program in Basel

We offer

• A Doctoral Position with primary supervision by Prof. Michael Nash and co-supervision by Prof. Niko Beerenwinkel, combining experimental and computational work
• Enrollment in the D-BSSE Doctoral program, with access to its coursework, seminar series, and interdisciplinary training environment
• A well‑equipped lab at Mattenstrasse 22, 4058 Basel with established protein expression, yeast display, AFM, and high-throughput infrastructure, plus support from lab senior scientific and technical staff
• Close mentorship from a PI with an established track record in protein engineering and single-molecule biophysics, and collaboration with a leading computational biology group
• Funding to attend international conferences and present your work
• An international and interdisciplinary team of energetics Doctoral Students, postdocs and scientists
• Exposure to ongoing projects in technology commercialization
• A collaborative, interdisciplinary project spanning molecular engineering, mechanobiology, high-throughput screening, and machine learning, with direct relevance to a clinical medicine
• Competitive salary in accordance with Swiss doctoral salary standards

Working, teaching and research at ETH Zurich

We value diversity and sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us - we are consistently working towards a climate-neutral future.

Curious? So are we.

Further information about the lab can be found on our Website. Questions regarding the position should be directed to Michael Nash, [e-mail] (no applications).