Postdoctoral Fellow in Machine Learning for Infectious Disease Diagnostics

Postdoctoral Fellow in Machine Learning for Infectious Disease Diagnostics
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We are seeking a highly motivated and skilled Postdoctoral Fellow in Machine Learning for Infectious Disease Diagnostics to join our dynamic and interdisciplinary research team. The successful candidate will apply machine learning (ML) and data science approaches to identify and define volatile biomarkers associated with bacterial activity in urine samples, with a focus on diagnostics and antimicrobial resistance (AMR) profiling.

Work at the interface of engineering, data science, microbiology and clinical research. Become part of a larger consortium together with Prof. Emma Slack (ETH Zurich), Prof. Adrian Egli (University Clinic Zurich), Prof. Thomas Kessler (University Clinic Zurich Balgrist), Prof. Andreas Günther (ETH Zurich), and Prof. Catherine Jutzeler (ETH Zurich).

Project background
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Urinary Tract Infections (UTIs) affect over 150 million individuals annually, ranging from mild symptoms to severe conditions such as pyelonephritis and urosepsis. Current diagnostic methods are time-consuming and require specialized knowledge and equipment, leading to delays and the overuse of broad-spectrum antibiotics that contribute to the growing AMR crisis. There is an urgent need for rapid, point-of-care diagnostics to address this challenge.

This project aims to develop a novel diagnostic device, progressing from pre-clinical validation to clinical implementation. By leveraging high-resolution volatilomics and machine learning, our goal is to identify minimal combinations of volatile biomarkers that can:

Distinguish sterile from infected urine
Identify key uropathogen species
Predict AMR profiles
Assess the risk of invasiveness (e.g., pyelonephritis and urosepsis)

Job description
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Key Responsibilities:

Develop and implement ML models to analyze high-dimensional metabolomics data
Design and validate predictive algorithms for biomarker discovery
Optimize data integration techniques for multi-omics and clinical datasets
Perform trend analysis of bacteria-containing samples over time to observe growth and mutation
Collaborate closely with interdisciplinary teams, including clinical partners, engineers, and microbiologists
Prepare manuscripts, reports, and presentations to disseminate findings

Profile
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PhD in Computer Science, Data Science, Machine Learning, Engineering, Biomedical Informatics, Bioengineering, or a related field
Proficiency in python programming 
Strong expertise in machine learning and deep learning frameworks (e.g., Keras, TensorFlow, PyTorch) and statistical modeling
Demonstrated ability to work independently and as part of a multidisciplinary team
Excellent written and verbal communication skills (Proficient in English)

Preferred Qualifications:

Familiarity with microbial genomics, genetic manipulation, or metabolic pathway analysis is a plus
Experience with mass spectrometry data analysis or metabolomics is highly desirable
Experience with cloud computing platforms and distributed computing tools
Experience with deep learning architectures such as CNNs, LSTMs, and transformers
A strong publication record in leading health/computer science journals or ML oriented conferences (NeurIPS, ICML, ICLR, ML4H)

We offer
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We offer a 2-year project-based contract that includes:

Opportunities to engage with different communities bridging data science and biomedical research leading to high impact publications
You will be part of a highly motivated, multidisciplinary and collaborative team
We will support your scientific career and application for postdoctoral fellowships on your path towards scientific leadership
You will have flexibility to develop your own line of research within the framework of this project
We encourage the attendance of relevant (inter-) national conferences to increase your visibility and present the project outcomes
You will be involved in the supervision of junior researchers and teaching in the lab
Access to state-of-the-art computational resources and collaborative research networks
Opportunities for professional development and career advancement
Working, teaching and research at ETH Zurich (https://ethz.ch/en/the-eth-zurich/working-teaching-and-research.html)

We value diversity
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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.

Curious? So are we.
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We look forward to receiving your online application with the following documents:

a letter of motivation (1-page max)
CV 
PhD diploma or equivalent

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Questions regarding the position should be directed to Prof. Catherine Jutzeler, by email at [email protected] (no applications).

We evaluate applications on a rolling basis. 

Starting date: April 1st, 20205

About ETH Zürich
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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.