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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
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. Project background Wearable technologies that analyze non-conventional biological matrices, such as interstitial fluid, promise longitudinal biomarker data with minimal invasiveness. These provide insights
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learning, XR, robotics or other technologies - to enhance processes across the entire value chain of new construction or renovation projects. Potential topics may include, but are not limited to: Data-driven
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100%, Zurich, fixed-term The Biomedical Data Science Lab investigates data-driven solutions for healthcare applications with a focus on neurological conditions such as spinal cord injury, lower
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chemical/process engineering, materials science, chemistry or closely related field Experience in nanomaterial synthesis and characterization Proficiency in X-ray absorption spectroscopy and data analysis
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Experience in nanomaterial synthesis and characterization Proficiency in X-ray scattering (X-ray diffraction, X-ray PDF) techniques and data analysis Demonstrated ability to work independently and as part of a
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position is part of a larger consortium bringing together engineering, data science, microbiology and medical expertise from ETH Zürich, University Zürich and Balgrist Hospital. Job description Design