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Field
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palaeobiology, ecology, geology, and fluid dynamics to test the potential ecological adaptations of different arthropod morphologies that may have allowed them to dominate life in Earth’s oceans. We will develop
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physiological signal. This MSc by Research will investigate a fundamentally different question: “Can low-energy electrical measurements provide a reliable estimate of changing fluid volume within a cardiovascular
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that capture ILC-specific morphology and biology while remaining robust to differences between hospitals, scanners, staining procedures and protocols. The researcher will investigate self-supervised and
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electronic test and measurement equipment, investigation of transient electrical behaviour, comparison of different device designs and fabrication variants, and support for the evaluation of device performance
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requirements for admission to the PhD programme Experience implementing and modifying deep learning architectures. Working knowledge of Python and a modern deep learning framework. Strong programming skills in
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different dyadic motor coordination tasks. A range of neurophysiological measures (EEG, ECG and fNIRS) as well as behavioural measures will be recorded simultaneously from both partners. Machine-learning
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include: - Extension to radio and optical diagnostics to test different interaction scenarios - Extension to low mass binary interactions at optical wavelengths - Development of empirically motivated models
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interest in the human brain. Programming experience (Python, MATLAB) and proficiency in spoken and written English is required. Experience with or an interest in microscopy, quantitative image analysis
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well as societal applications in cancer treatment, radiation-tolerant microelectronics, and other radiation-based technologies. Duties The doctoral project will investigate the competition between different reaction
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central scientific challenge will be to learn integrated representations of forest ecosystems from datasets with very different characteristics, resolutions, coverage, and levels of supervision