高通量蛋白互作分析博士生
Doctoral Student in High-throughput Analysis of Protein-Protein Interactions
开发高通量蛋白互作力学稳定性检测平台
0.00
面试概率评分
核心匹配 0.00
项目相关性
0.00
关键技能覆盖
0.00
形式资格
0.00
偏好加分
0.00
关键缺口
失格
评分理由
The candidate profile section is empty, so no educational background, experimental methods, or computational skills can be verified against the position's requirements (Master's degree, molecular biology/protein expression, yeast display, sequencing analysis, Python). With no evidence of methodological or domain overlap, the project_relevance and critical_skill_coverage signals default to zero. This absence of any verifiable information is treated as a fatal shortlisting gap, though it reflects missing input data rather than a confirmed deficiency in a real applicant. · 迁移评估:No candidate profile provided; transferability cannot be established
软性注意
- 有酵母展示、定向进化或单分子生物物理(如AFM)经验者优先,但非必需
- 有机器学习经验者优先
- 需要愿意承担本科教学/化学实验课辅导
数据完整(5/5),可生成高质量材料
PI 信息
主导师
Michael A. Nash
直接提取
院系
Nash Lab (Lab for Engineering Synthetic Systems), Department of Chemistry, University of Basel / Department of Biosystems Science and Engineering (D-BSSE), ETH Zurich
PI 近期论文(10 篇)
标记为「重要」或「很重要」的论文会进入 MV/CV 生成上下文;「一般」和「忽略」不进入。每次生成都读取最新标记。
[2026] Decoding the substrate specificity landscape of a promiscuous enzyme through multi-substrate mutational scanning
通讯作者
[2026] Enzyme-Responsive Hemostatic Elastin-like Polypeptides for Fibrin Stabilization and Coagulation Restoration in Thrombocytopenia
通讯作者
[2026] Signatures of Electron–Hole Hopping in Myoglobin Peroxidase Activity Revealed by Deep Mutational Learning
通讯作者
[2026] Enzyme proximity sequencing: mapping multidimensional enzyme fitness at scale
通讯作者
[2026] The Candida auris Adhesin Scf1 Mediates Broad Host-Protein Recognition through Shared Adhesive Regions
通讯作者
[2025] Multi-state catch bond formed in the Izumo1:Juno complex that initiates human fertilization
通讯作者
待分析
[2025] Signatures of Electron-hole Hopping in Myoglobin Peroxidase Activity Revealed by Deep Mutational Learning
通讯作者
手动添加论文 DOI
职位描述 / 要求
职位描述
100%, Basel, fixed-term
print
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 plus
Independent, 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
Workplace
Workplace
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
chevron_right
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.
We look forward to receiving your online application with the following documents:
Cover letter addressed to Prof. Michael Nash, describing what you have achieved thus far, which direction you want to work in, and why (one to two pages)
Detailed CV, including a list of any publications or preprints
Transcripts of all degrees in English, with the local grading scale, which ETH requires for doctoral admission
Your Master's thesis or a representative project report, in English. A draft or an in-progress version is fine if you have not finished yet
Up to two of your publications or preprints, if you have them. We do not expect publications at this stage, but if you have written something we would like to read it
Names and contact information of at least two references, usually your thesis supervisor (Reference letters are optional)
Further information about the lab can be found on our
Website
. Questions regarding the position should be directed to Michael Nash, michael.nash@bsse.ethz.ch (no applications).
Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.
About ETH Zürich
ETH Zurich is one of the world’s leading universities specialising in
science and technology. We are renowned for our excellent education,
cutting-edge fundamental research and direct transfer of new knowledge
into society. Over 30,000 people from more than 120 countries find our
university to be a place that promotes independent thinking and an
environment that inspires excellence. Located in the heart of Europe,
yet forging connections all over the world, we work together to
develop solutions for the global challenges of today and tomorrow.
Curious? So are we.
We look forward to receiving your online application with the following documents:
Cover letter addressed to Prof. Michael Nash, describing what you have achieved thus far, which direction you want to work in, and why (one to two pages)
Detailed CV, including a list of any publications or preprints
Transcripts of all degrees in English, with the local grading scale, which ETH requires for doctoral admission
Your Master's thesis or a representative project report, in English. A draft or an in-progress version is fine if you have not finished yet
Up to two of your publications or preprints, if you have them. We do not expect publications at this stage, but if you have written something we would like to read it
Names and contact information of at least two references, usually your thesis supervisor (Reference letters are optional)
Further information about the lab can be found on our
Website
. Questions regarding the position should be directed to Michael Nash, michael.nash@bsse.ethz.ch (no applications).
Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.
Apply online now
About ETH Zürich
ETH Zurich is one of the world’s leading universities specialising in
science and technology. We are renowned for our excellent education,
cutting-edge fundamental research and direct transfer of new knowledge
into society. Over 30,000 people from more than 120 countries find our
university to be a place that promotes independent thinking and an
environment that inspires excellence. Located in the heart of Europe,
yet forging connections all over the world, we work together to
develop solutions for the global challenges of today and tomorrow.
Let’s connect
职位要求
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 plus
Independent, 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
申请材料(AI 定制)
未生成点击下方按钮,AI 将基于你的背景和该职位需求生成定制的 CV 和 MV(Word + PDF)。约需 20-40 秒。