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Field
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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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the synchrotron-based imaging technique Dark-Field X-ray Microscopy and together we utilize it to visualize the evolution of internal structures in metals during plastic deformation, i.e. changes in shape due
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to research this topic. Interest in laboratory work and basic technical understanding. Fluent written and spoken English. Programming skills in e.g. Python, R, Matlab and Java. Experience in image processing
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for calculation and design Design and optimization of a flexible oxygen supply Optimization of the pilot plant based on test and simulation results Development of design and process engineering solutions
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See advertisement About the position A Doctoral Research Fellowship in Open-Ended Artificial Intelligence is available at the Faculty of Computer Science, Engineering and Economics at Østfold University
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Qualifications Master or Honours 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
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, biology, engineering, machine learning / data science, coding. How to apply: This is an Expression of Interest process. To express your interest in applying, candidates must supply the following information
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discrimination but with international mobility requirement, with interest in fluorescence, microscopy, photochemistry, photophysics, data treatment and image analyses. Goals of the PhD project include
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at the same time so special. The originality of the experiments is in the combination of X-ray based scattering and imaging methods to monitor the changes at the particle scale during testing. Research
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technologies. Metamaterials, engineered to exhibit properties not found in naturally occurring materials, offer an innovative pathway to overcome these limitations. By designing intricate periodic or quasi