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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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A continual learning approach for robust robotic control in electric batteries assembly. This project is an exciting opportunity to undertake industrially linked research in partnership with
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machine learning algorithms and to assess when AI predictions are likely to be correct and when, for example, first principles quantum chemical calculations might be helpful. Predicting chemical reactivity
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Open PhD position: Autonomous Bioactivity Searching Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 42-month funded PhD studentship will contribute to cutting
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, chemistry, and be willing to learn new disciplines and innovate to achieve the project goals. Additionally, ideal candidates would also have interests in areas such as: 3D printing, materials sciences
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, chemistry, and be willing to learn new disciplines and innovate to achieve the project goals. Additionally, ideal candidates would also have interests in areas such as: 3D printing, materials sciences
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, chemistry, and be willing to learn new disciplines and innovate to achieve the project goals. Additionally, ideal candidates would also have interests in areas such as: 3D printing, materials sciences
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to learn new disciplines and innovate to achieve the project goals. Additionally, ideal candidates would also have interests in areas such as: 3D printing, materials sciences, or physics & electronics, and
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manufacturing and additive manufacturing. Requirements: Candidates should have a background in one of: engineering, materials science, chemistry, and be willing to learn new disciplines and innovate to achieve
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plus Familiarity with FE simulation tools such as ANSYS or Abaqus (or willingness to learn) General knowledge of structural analysis and material behaviour, especially failure mechanisms Some experience