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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
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-term adaptation 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
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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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mechanical accuracy for small organic molecule energetics, but are too slow for routine use in medicinal chemistry. This collaborative, industry-funded, computational project will i) use state-of-the-art
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performance of AI models for fall detection. The research will combine experimental studies on different floor systems, finite element simulations of vibration propagation, and AI-based signal analysis
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productivity and energy efficiency. Bioprocess modelling: Employ simulation, techno-economic analysis (TEA), and life-cycle assessment (LCA) to assess cost and GHG performance. Candidate’s Competencies and
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product quality, reducing waste, and enabling sustainable, low-carbon manufacturing of high-value materials. Academically, the work will contribute to the growing field of multiphase flow modelling. Contact
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of the following: numerical methods, high-performance computing (HPC), Computational Fluid Dynamics (CFD), applied mathematics, physics, engineering or subsurface flow modelling. Enthusiasm
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we place value on prior experience, enthusiasm for research, and the ability to think and work independently. Excellent Analytical skills and strong verbal and written communication skills are also