513 machine-learning-and-image-processing-"RMIT-University" Postdoctoral positions in United Kingdom
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discipline (eg Statistics, Machine Learning, Biostatistics, AI, Engineering) with experience of developing and applying new methods. You will be able to develop research projects, with publications in peer
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of virus evolution into mathematical models of spillover infection and transmission of prototype viruses representing viral families concern to support the development of methods for virus sequence analysis
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gases, including the operation of magneto-optical traps, the production of quantum degenerate gases and optical trapping using optical tweezers/lattices. They will be expected to display initiative and
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computer codes to solve some of the daily research problems and have experience with high performance computing. You should have a PhD in Chemistry, Physics or Materials Science with a proven research track
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of career. Experience in using qualitative data analysis computer software such as NVivo would be an advantage. Skills 13. Demonstrable ability to plan and manage research, both independently and as part of a
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neuroscience or a related field. Experience of MEG/OPM-MEG acquisition and data analysis is essential. Experience with MATLAB or Python computer programming for neuroimaging data analysis is essential
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knowledge in laser spectroscopy and imaging, ideally in vibrational or acoustic microscopy. Knowledge of mechanical testing and complementary molecular spectroscopy methods is desirable. Please ensure you
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ZEISS LLSM, ZEISS Elyra, ZEISS 980 confocal, PicoQuant FLIM, PicoQuant Luminosa, and bioimage analysis of fluorescence microscopy images. You must have the ability to manage your own academic research and
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of papyri and related material in Greek, Latin, Demotic and Coptic, as well as images and 3D reconstructions of relevant artefacts. The postholder will be responsible for the Coptic material. As
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on the development of a computational setup for fiber-enriched materials design within the Processing-Structure-Properties-Performance (PSPP) framework under the joint supervision of Assistant Professor Sergei Khakalo