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team including the PI, graduate students, and external collaborators. Responsibilities include sample collection (core facility visit), laboratory analyses, data interpretation, thermal history modeling
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cladding processes, including parameter optimisation and advanced monitoring techniques. Develop and refine process models, simulations, and automation strategies. Characterise microstructure, wear behaviour
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development and distributed sensor networks, climate modeling, and Artificial Intelligence/Machine Learning. The position also offers meaningful engagement with academic, industry, farming communities and
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execute mechanistic experiments in vascular and inflammatory biology. Perform in vivo studies using murine models such as atherosclerosis, fatty liver, and acute lung injury. Conduct cell and organoid
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Postdoctoral Researcher with a strong computer science background and demonstrated expertise in deep learning and generative model development to lead the AI component of this initiative. Responsibilities and
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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intelligent decision architectures, predictive analytics, and adaptive computational models that can operate in dynamic, uncertain, and high-stakes project environments. The appointee will conduct original
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/modelers, and data scientists Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of Materials Science, Chemical Engineering, Chemistry, or a closely related field
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processes and particle acceleration in geospace. Research activities will integrate observational data analysis, theoretical modeling, and numerical simulations to advance our understanding of magnetospheric
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. This role focuses on the aerodynamic modelling of floating wind turbines and their dynamic response under realistic wind and wave conditions. You will conduct high-fidelity CFD simulations of floating