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Field
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Qualifications* PhD Degree in Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, or a related field Familiarity with (biomedical) signal processing Experience working with clinical data
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, biophysics, physical chemistry, engineering, or a related area, or have submitted a PhD thesis prior to taking up the appointment. The research requires strong interest in and knowledge of the broad area of
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workflows of bioimaging analysis to quantify advanced imaging data generated by super-resolution STED microscopy, FLIM, FRET and FCS. Candidates must hold a PhD in cell biology, biophysics or biochemistry
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graph neural networks for complex sensor networks such as those involved in brain imaging Develop and test data-driven methods for image and video processing for microendoscopy. Key Duties and
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combustion experiments and in particular hydrogen and liquid fuels, image processing, and excellent knowledge of turbulent combustion. Appointment at Research Associate level is dependent on having a PhD
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imaging applications by ensuring the Centre’s high-end commercial systems and bespoke in-house development platforms remain at the forefront of current technology. This includes providing expert advice
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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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will focus on biosafety and in-vivo imaging aspects of the project, and will be hosted by the Functional and Molecular Imaging Group (Prof. Razansky). The second PhD student (this position), hosted by
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learning technology/AI as a tool to analyze these images and come up with an algorithm to be utilized for early detection of AD/PD when the patient has no visible sign and symptoms. This project is team
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: Apply ultrafast and nanosecond laser processing techniques to engineer surfaces with improved thermal performance. Develop micro/nanostructures to enhance infrared radiation and conductive heat transfer