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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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for “what-if” scenario analysis derived by Integrated Assessment Models (IAM). You will develop different pathways based on multiple realisations of CMIP6 and/or CMIP7 datasets, aligned with IPCC scenarios
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from largely unlabelled data that can generalize across different radar configurations and be efficiently adapted to multiple downstream perception tasks with limited labelled data. An important research
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of large-scale biological datasets will be considered a strong advantage. Experience with CRISPR-based approaches or statistical programming in R or Python is desirable but not required. The application
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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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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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reduces the reproducibility of scientific research, as different researchers make different decisions. A Bayesian multiverse is a systematic way of reducing these adverse effects and aims to make
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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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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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high-quality experimental datasets using laboratory flume facility. Developing and validating hydrodynamic models to simulate the effect of vegetation in different environmental conditions. Designing and