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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
based on experimental and computational data. Establish predictive models based on artificial intelligence methods. Utilize the developed models to design and optimize the properties of materials. Conduct
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Researcher, Royal College of Art Would you like to be part of a major collaborative research initiative to help tackle transport’s contribution to the climate crisis? Are you looking to apply your design
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Department of Computing Research Assistant (Ref. 260414011) [Appointment period: twelve months] Duties The appointee will assist the project leader in the research project – “A multimodal
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to the broader Innovation and Knowledge Centre mission to help bridge the gaps between academic research and industrial impact in this field. A strong background in machine learning, computer engineering, applied
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Intelligence (HAI) Medicine, Biomedical Informatics Research (BMIR) Postdoc Appointment Term: 1 year with potential to renew Appointment Start Date: June 2026 Group or Departmental Website: https
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empirical research, contributing to innovative approaches in technology-enhanced language assessment. A. Required background and skills Bachelor’s degree in computer science, artificial intelligence, data
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design, and help predict failure mechanisms during LAT production. There is also scope to develop coarse-grained computer simulations (e.g. dissipative particle dynamics) to expand the chemical and
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The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational
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, including cell culture, flow cytometry, ELISA, qPCR, Western blotting, and immunohistochemistry. Read literature and help with experimental design. Troubleshot research issues such as assisting with resolving
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involves considering which bodies get designed for and are expected by robots as solidified in their design. The postdoc will be conducted within the Wallenberg AI, Autonomous Systems and Software Program