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Field
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training dataset of well-studied volcanoes with known large eruptions, the project will employ statistical and machine learning (ML) methods to identify the strongest predictors of eruption magnitude
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a Federated Learning approach to deploy the AI, ensuring robust privacy preservation of sensitive student data. The successful applicant will undertake advanced statistical analysis and stakeholder
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opportunity to devise an exciting research project, to receive training in data capture and manipulation, statistics, trait analysis, and modelling of interaction webs, and to undertake fieldwork
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flows, and to have developed skills in experimental fluid mechanics, statistics, data processing, machine learning, and mathematical modelling. Supervisors: Dr Kostas Steiros Duration: 3.5 years. Funding
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and collaborative partnerships. They will receive interdisciplinary training across microbiology, statistics, as well as working with policy stakeholders to translate research into real-world
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. dissertation, research project, report) – this could be within an academic or professional setting. Maths or Physics A-level *Significant quantitative component examples include statistics, bioinformatics, maths
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To achieve these tasks, the student will receive training in field collections and community engagement, specialist laboratory techniques and data/statistical techniques in two phases: (1) Using on-going data
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knowledge/experience of qualitative research methods (e.g. interviews/focus groups, thematic analysis) and quantitative methods (e.g. collection/statistical analysis of survey data, development
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application per studentship, you cannot apply for multiple studentships on one application. Contact Details Dr Adam Ingram School of Mathematics, Statistics and Physics Email: adam.ingram@newcastle.ac.uk
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to create molten salt MLIPs, predicting a variety of industrially pertinent properties: thermal conductivity, heat capacity, viscosity, and thermodynamic phase data. We will develop new statistical mechanical