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, interpretation, and predictive modelling. We therefore seek a new appointment to add capacity to our expertise in this area. We have particular interest in, but are not restricted to, expanding our data science
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sustainability. The selected researcher will contribute to the development of predictive models and machine learning algorithms for data analysis from plant-based sensors, multispectral and thermal imagery, and
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exploratory analysis on large, multi-dimensional datasets; (b) develop predictive/diagnostic models and algorithms to lead and support clinical/translational research; (c) collaborate with cross-functional
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to improve predictive models and inform design strategies. Work in Practical Settings — engage directly with NIHE to implement and test research methods in operational housing schemes. This work will equip
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methods to understand and predict the adsorption, self-assembly, and protective behavior of N-heterocyclic carbenes (NHCs) on metallic and oxidized surfaces. NHCs are promising corrosion-inhibiting
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factors such as prediction of plant growth, water pollution, and environmental biodiversity loss. The approach seeks to create robust, explainable models that reflect domain-specific insights, advancing
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appropriate balance of synergy and complementarity with materials science and engineering research already underway. Alignment with Materials Ageing, Performance, and Lifetime Prediction and a willingness to
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integrating modeling, machine learning (ML), and advanced control methodologies. The research will focus on designing AI-driven algorithms to assess battery health, predict degradation trends, and optimize
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time. In this project, we propose a method for identifying and classifying such emerging asynchronous trends. The goal is to be able to predict how a new emerging trend will develop using similar
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have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models for Alzheimerâ™s, Parkinson, and Dystonia as well as to identify novel