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therefore investigate how these variations affect signal detectability and classification performance of AI models for fall detection. The research will combine experimental studies on different floor systems
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) sensor data. This will be a small system-on-chip designed to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated
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engineering, physics and applied mathematics. You should have experience in one or more of the following: numerical methods, high-performance computing (HPC), Computational Fluid Dynamics (CFD), applied
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(TEA), and life-cycle assessment (LCA) to assess cost and GHG performance. Candidate’s Competencies and Skills Background in biochemical or chemical engineering, biotechnology, or microbial processes
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benefit Krystal Technology by improving product quality, reducing waste, and enabling sustainable, low-carbon manufacturing of high-value materials. Academically, the work will contribute to the growing
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pathways that will help to navigate severe socio-economic disruption from drought events. This PhD studentship is part of the Water Infrastructure & Resilience (WIRe) CDT funded by EPSRC and Anglian Water
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-benefit infrastructure solutions to support long-term water security. Funded by School of Engineering, Newcastle University , this PhD studentship is part of the Water Infrastructure & Resilience (WIRe) CDT
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outputs. This is an exciting opportunity to work at the cutting edge of AI research. Your work will support safe, interpretable, and sustainable AI deployment for healthcare, environmental monitoring, and
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to operate on the edge (i.e. close to the sensor). The project will explore whether emerging logic-based ML algorithms can be translated into smaller, faster, more energy efficient and cost-effective hardware
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multi-modal AI models that can effectively handle uncertainty and generate reliable outputs. This is an exciting opportunity to work at the cutting edge of AI research. Your work will support safe