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Research Fellow - Advanced Signal Processing and Machine Learning Techniques for Vital Signs Measurement from Video Images Job No.: 691722 Location: Clayton campus Employment Type: Full-time
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-field imaging of dynamic processes" "Multi-scale X-ray speckle-based imaging" "Spectral X-ray speckle-based imaging" "Single-shot multi-projection X-ray phase-contrast imaging" "X-ray virtual histology
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‑edge imaging research. In this hands‑on specialist role, you’ll operate, maintain and optimise our advanced optical, fluorescence and confocal systems while delivering high‑quality research support, user
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; wireless sensor networks; green communications; next generation wireless broadband networks; signal processing and acoustics; digital image processing; structural health monitoring and underwater
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-disciplinary team of clinician scientists and computer scientists to develop diagnosis/predictive/treatment/robotics surgery models of diseases of interest using multimodal medical data, consisting of images
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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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maritime heritage] [Image changes to show four crew members standing on the deck of the Investigator next to a multi-corer and text appears: Studying ocean processes and productivity] [Image changes to show
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This project aims to employ advanced machine learning techniques to analyse text, audio, images, and videos for signs of harmful behaviour. Natural language processing algorithms are utilized
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, signal processing and inverse modelling. The role is based at the Image X Institute and will be led by Dr Chandrima Sengupta. You will benefit from a strong mentoring environment; lead and be part of high
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Deepfakes, derived from "deep learning" and "fake," involve techniques that merge the face images of a target person with a video of a different source person. This process creates videos where