Postdoctoral Fellow in Machine Learning for Infectious Disease Diagnostics

Postdoctoral Fellow in Machine Learning for Infectious Disease Diagnostics 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 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 Predict AMR profiles Assess the risk of invasiveness (e.g., pyelonephritis and urosepsis) 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 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) Workplace 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 the 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 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. 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 (no applications). We evaluate applications on a rolling basis. ETH Zurich is one of the world’s leading universities specializing 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. #J-18808-Ljbffr