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Swinburne’s strategy draws upon our understanding of future challenges. We choose to build Swinburne as the prototype of a new and different university – one that is truly of Technology, of Innovation and of
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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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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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of Technology Swinburne’s strategy draws upon our understanding of future challenges. We choose to build Swinburne as the prototype of a new and different university – one that is truly of Technology, of
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, machine learning, electrical engineering, or a related field. The ideal candidate will have some of the following skills such as: Computer vision and machine learning: A solid understanding of image and
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, performing data collection and processing of cryo-EM data. Setup of future hardware and software as the research technology is upgraded. Providing lab members support in data collection, image processing, map
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described through five overall research areas: Diagnostic Imaging, Digital Health, Personalised Therapy, Precision Diagnostics, and Sensory and Neural Technology. Our technologies and solutions are developed
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highly motivated Doctoral Research Fellow in a full time (100 %) fixed term position available for three (3) years at the Faculty of Computer Science, Engineering and Economics, Department
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Technology, Campus Norrköping. Your work assignments This position is motivated by the need for reliable visualization and data analysis methods that support understanding of the increasing amount
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University of Technology. About Swinburne University of Technology Swinburne’s strategy draws upon our understanding of future challenges. We choose to build Swinburne as the prototype of a new and different