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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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, 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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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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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
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of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging