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or machine learning to complex data. The successful candidate will have (or be nearing completion of) a PhD in a relevant field such as polymer science, materials engineering, or mechanical engineering
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techniques to enable large-scale data search across Personal Online Datastores (pods) hosted on distributed pod servers, addressing both keyword-based search and SPARQL querying. EPRESSO will build on and
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Sussex University in collaboration with ASG and other partners. You will hold a PhD in a related discipline, such as business, management, economics, or engineering. This is a demanding project and you
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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-year tenure. These grants support the costs of academic materials, travel expenses, computer equipment, books and costs relating to organising a conference or workshop in St John’s. Fellowships are held
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approaches, machine learning) where appropriate. The successful candidate will actively promote FAIR data practices and will have opportunities to contribute to teaching, training, and wider community
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or machine learning to complex data. The successful candidate will have (or be nearing completion of) a PhD in a relevant field such as polymer science, materials engineering, or mechanical engineering
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with physical objects/environments, and audio rather than video based AR can help enhance memory/learning Humanizing Computer Mediated Communication: Synthesizing co-presence - What does it mean to feel
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transcriptomics data analysis and interpretation Desirable criteria Contribution to open-source bioinformatics tools Hands-on experience with machine-learning frameworks Downloading a copy of our Job Description