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the area of end-to-end modular autonomous driving using computer vison and deep learning methods. This includes developing an efficient and interpretable image processing, vision-based perception and
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constructively with students, colleagues, and external stakeholders Whether you’re progressing in your academic career or bringing new energy from a postdoctoral or teaching-focused background, you’ll thrive in a
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, in various formats including images, texts and numeric values. The study of these unstructured data in the pathology laboratory information system (LIS), together with the information from
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displacement and the length of such deformations (or displacements). As later steps, we will then look at time series data (or movies of images) and also at out-of-plane 3-dimensional deformations. This will
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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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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
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methods, the goal is to enhance the ability to identify and mitigate the risks posed by fraudulent online platforms. Required knowledge Python programming Machine learning background Text analysis Image
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from standard camera imagery. Such spatial computing applications represent the most significant paradigm shift in human-computer interaction (HCI) since the introduction of graphical user interfaces
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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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at primary care and offer optimal use of scarce health system resources. The model will be trained using skin images (clinical and/or dermoscopic) to identify disease relevant features and accurately diagnosis