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project funded by the Leverhulme Trust. Description below: The density of information content in screen media – i.e., the light, volume and pitch changes from which we extract meaning – has increased
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but not essential. A strong background in materials science and/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition
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models on edge devices Reporting findings and methods in conference and journal papers Your profile Masters, Diploma or equivalent degree in IT/computer science/statistics/applied mathematics/data science
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, informed by the characteristics of the technology of delivery; • To validate profiles using available demand data; • To integrate barriers and enablers into the model of ASHP installation. Person
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-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those
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and/or Python, is highly desirable. Experience of collecting neuroimaging and/or wearables data from infants/children is also desirable. The project will be based at the Institute for the Science of
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of collecting neuroimaging and/or wearables data from infants/children is also desirable. The project will be based at the Institute for the Science of Early Years (www.isey.org ) in London, UK. Supervision will
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Posting Details Title Information Assistantship Title Graduate Research Assistant Job Class Code 10095 FLSA Exempt Minimum Qualifications Minimum Qualifications 1. Currently admitted to the Graduate
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the field of Computational Morphodynamics in plants. The work will be within the ERC-funded project RESYDE (https://resydeproject.org ) with the aim of building a virtual flower using multi-level data and
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. Development of DT information modelling, data fusion, and forecasting guidelines and standards, and technology maturity benchmarks to derive cloud platform maturity level standards. Lead on the development