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, consumers, journal editors and guideline developers. Project 2: Integrating living evidence into adaptive platform trials to create efficiencies in research generation This project aims to explore
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Apply now Do you want to work at the intersection of root development and plant-microbe interactions and discover how microbes induce stress-protective layers in crop roots? This PhD position might be a
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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive
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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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this knowledge to suggest new ways to optimise catalytic reactions. Overall, you will contribute to the development of AI-enhanced autonomous workflows for interface. These implementations will be general and will
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measure gravity of microscopic systems?”. Such questions and their implications for the foundations of physics are the driving force behind our research. You will be part of our team and develop new ideas
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. The goal of this project is to advance gene regulatory network (GRN) inference from multi-omics data by developing novel AI techniques that exploit the knowledge of gene perturbations (experimental design
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permeability are developed for viscous-, gravity-, and capillary-dominant cases, and for combined cases. Dissolution of CO2 in water along with capillary, chemical and stratigraphic entrapments are not accounted
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and interests. Possible research directions include constructing multiscale simulation workflows and applying them to materials design, and/or developing and implementing novel machine learning
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, which frequently produces designs that are suboptimal when subjected to real-world dynamic environments. Although a handful of advanced, high-fidelity solvers have been developed to tackle this issue