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achieve an average of 65% or higher in your MSc taught assessments to be considered for progression to the PhD studies. Note: you will not graduate with an MSc. If you meet the progression criteria, you
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which the training data sampled. This PhD study will address this research challenge. At a glance Application deadline: Ongoing Duration of award: 3 years Eligibility: EU, UK Reference number: CRAN1148
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overseas. Training can be provided in computational fluid dynamics, machine learning, and nonlinear dynamics. These skills are highly valued across a wide range of industries. Recent data reveals that Fluid
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data sources to answer important population-level questions that will inform health technology assessment, policy, and practice. The successful PhD student will be embedded within an interdisciplinary
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. This PhD will suit candidates with backgrounds in psychology, cognitive science, linguistics, or data science, with experience coding in R or Python, natural language processing, and experimental design
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Characterisation" "Data Science and Machine Learning in Materials" "Plastics Recycling and Circular Economy" How to apply: https://uom.link/pgr-apply-2425 How many positions: 1 This 3.5 year PhD is open to UK
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Short overview of PhD opportunity The PhD studentship aims at studying and developing more comprehensive user interfaces for computer-aided decision-making processes, which can benefit from 3D/360
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forecast local generation and demand. To do so, historic local data (e.g., the active buildings in Swansea University) and Met Office data will be used to train and validate the proposed ML model
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, analysis, chain of custody and reporting standards) is compatible with a human rights framework. This PhD project will analyse, and where needed proffer, good governance in relation to data and sample
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; Midlands Graduate School Doctoral Training Partnership | Nottingham, England | United Kingdom | 9 days ago
the consumption journey. This exciting PhD Programme will look to address these challenges by employing a novel combination of data-donation, data-enhancement, and data-augmented laboratory experiments. The project