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Overview: As data becomes more accessible, new challenges arise around how best to use it—especially in complex, multi-system environments like aerospace. Ontologies offer a powerful solution by
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techniques, theoretical modelling, and precise synthetic control – have significantly improved the activity, selectivity, and stability of CO₂-to-alcohol conversion. Key catalyst systems include Cu-based
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 2 hours ago
. Applicant must have (or expect to obtain shortly) a Ph.D. in Biological Sciences or related field. Experience with Neuroimmunology and/or Immunology is highly preferred. Proficiency in mouse models of disease
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modelling • A high fascination for technical/scientific problems of theoretical and numerical research • Experience in the field of numerical time-accurate flow simulation, preferably turbomachinery • Ability
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problem-solving skills and deep expertise in the development of complex computational models. Candidates who have not yet acquired their PhD would be appointed at the Research Assistant level. The
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Overview: Project Overview Investment casting is an ancient yet vital manufacturing technique, especially in the aerospace sector where thin-walled, complex components are increasingly required
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friendly catalysts and substrates. We use NMR spectroscopy for serendipitous discoveries and unbiased characterizations of molecules, their conversion, and their interactions in complex systems. Solvent
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About the role The Department of Mathematics at the University of Sussex is inviting applications for a fixed term early career research position centred on advancing ecological modelling of soil
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for existing medications, a strategy that can significantly reduce the time and cost required to bring a drug to market. The project will use AI models such as CycleGANs, that by learning from complex spatial
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
intelligent methods that integrate large language models (LLMs) and knowledge graphs to interpret technical documentation and structure complex engineering knowledge. The goal is to create digital twins