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of Engineering at the University of Birmingham and delivered in collaboration with industrial partners and the Quantum Technology Hub in Sensing, Imaging and Timing (QuSIT) – one of five national hubs funded by
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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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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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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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engineering, medical physics, image analysis, computational neuroscience or quantitative psychology is desirable. If you would like to work on a PhD project about biological understanding of human diseases and
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complete picture of fish habitat use and connectivity. The PhD is part of the section for Ecosystem based Marine Management and the Marine Habitats research group, as well as several synergistic initiatives
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Biomedical Engineering conducts leading research in image analysis, computer vision, and machine learning, with a growing emphasis on generative AI and AI for scientific discovery. Our mission is to develop
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. ✔ Close supervision by leading experts in bioelectroanalysis: 250+ publications (85% in Q1, 80% in D1 journals) 5 international patents Multiple technology-based startups ✔ Multidisciplinary research
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: Education Ph.D. in Engineering, Chemistry, Biology, Physics or a relevant field. For information on certificates and diplomas issued abroad, please see Degree equivalency Experience Significant* experience in
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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