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Project The Adaptive Design for AI-Driven Processes in Transforming Dynamic Landscapes (ADAPT) project develops scalable, data-driven design methodologies that transform dynamic environmental processes
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relevant subject (molecular microbiology, cell biology, immunology etc) * Prior experience with bacterial pathogens Proven ability to design, implement and analyse experiments Experience with eukaryotic cell
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relevant subject (molecular microbiology, cell biology, immunology etc) * Prior experience with bacterial pathogens Proven ability to design, implement and analyse experiments Advanced molecular microbiology
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: Essential criteria PhD qualified in relevant subject (molecular microbiology, cell biology, immunology etc) * Prior experience with bacterial pathogens Proven ability to design, implement and analyse
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the interfacial strength, nano-scale 3D structure and residual stresses in advanced power electronic materials (INTERVENE), and is fixed-term of 18 months. Power electronics is a major technology sector for the UK
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the interfacial strength, nano-scale 3D structure and residual stresses in advanced power electronic materials (INTERVENE), and is fixed-term of 18 months. Power electronics is a major technology sector for the UK
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following skills and experience: Essential criteria PhD in a relevant subject (molecular microbiology, cell biology, immunology etc) * Prior experience with bacterial pathogens Proven ability to design
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relevant subject (molecular microbiology, cell biology, immunology etc) * Prior experience with bacterial pathogens Proven ability to design, implement and analyse experiments Advanced molecular microbiology
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both institutions and industry partners. About the Project You will join FabricFlow – an innovative AI-powered ecosystem that revolutionises how fabrics are tracked and managed in the fashion industry
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, immunology etc) * Prior experience with bacterial pathogens Proven ability to design, implement and analyse experiments Experience with eukaryotic cell culture systems Experience with in vivo infection models