Postdoctoral Fellow in Machine Learning for Infectious Disease Diagnostics
Vor 5 Tagen
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 backgroundUrinary 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)
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
- 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 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
- Be part of a highly motivated, multidisciplinary and collaborative team
- Support for your scientific career and application for postdoctoral fellowships on your path towards scientific leadership
- Flexibility to develop your own line of research within the framework of this project
- Encouragement to attend relevant (inter-) national conferences to increase your visibility and present the project outcomes
- Involvement 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
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