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to study chromatin and gene regulation in mammalian cells and human disease systems. Current ongoing projects include: statistical modeling and advanced machine learning/AI method development for predicting
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FLAME-GPU accelerated agent-based modelling of material response to environmental and operational loading EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce
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computational methods to address and derive theoretical models and predictions. For this line of research we are seeking several postdoctoral researchers to work synergistically both within the team and with our
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image analysis and sensor technologies (e.g. RGB/NIR) for textile production, as well as using machine learning for process optimisation and performance prediction from fibre to finished product
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, assess the health state of systems, and predict their future evolution and remaining useful life. The proposed approach integrates physics-based and data-driven modeling techniques, including machine
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modeling, machine learning, or data-driven prediction methods applied to environmental datasets. Experience building and maintaining large, frequently updated archives of weather or climate observations
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, regulatory, or multimodal biological data. Support target and mechanism prioritization by integrating model predictions with biological knowledge and external data sources. Work closely with academic partner
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and researchers to gain insight into novel methods used to predict toxicity of various chemicals and gain understanding of how these chemicals impact in vitro, cell-based model systems. Why should I
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)—to enhance decision-making in dynamic environments. ML predicts load variations and failures, SDN enables centralized resource management, and NFV supports flexible service deployment.This thesis project
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large dataset of P. aeruginosa genomes and experimental metadata to predict key mutations to the organism. The postdoctoral researcher will join the Whelan lab led by Dr. Fiona Whelan. The Whelan lab is a