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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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informatics, biomedical engineering, statistics, or related fields. The lab is engaged in developing novel deep learning and AI-based technologies for digital biopsies from medical images and real-world
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
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Research Studentship in ‘Deformation and fracture of TRISO fuel particles’ 3.5-year DPhil studentship Supervisor: Prof Dong Liu, Prof Emilio Martinez-Paneda About the Project The proposed PhD
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Position Description The Unsteady Flow Diagnostics Laboratory (UNFoLD) led by Prof. Karen Mulleners at EPFL in Lausanne is looking for multiple PhD students to join the group in the fall of 2025 or early
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, sports, food safety, and environmental monitoring. By integrating electrochemical techniques and imaging technologies, the unit delivers cutting-edge solutions with real-world impact. Led by Prof. María
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(bio-)chemistry, physics, and engineering expertise to study molecules and cells, taking advantage of optical and single-molecule imaging, molecular probes, molecular biology, and 'large' data analysis
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training that prepares young scientists for a successful career in infectious disease research. The LIV technology platforms offer state-of-the-art infrastructure for flow cytometry, microscopy and image
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using established in vitro digestion models, gut imaging techniques, and in vivo assessment of metabolic and appetite effects in healthy volunteers. The acquired knowledge can be used to inform
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the mobility, ubiquity, security, and interactivity of computers, data, software, and users. The pervasive computing paradigm enables technologies such as sensors, actuators, and computers to take a back seat