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Field
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industry and academia together to drive pre-competitive, fundamental research in polymers. We welcome applicants with interests in polymer physics, materials processing and characterisation, machine learning
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econometric and machine learning frameworks to analyse the causal relationships between transport behaviour, residential energy consumption, and environmental outcomes in urban systems. The successful candidate
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mechanisms, Sensors and instrumentation, Data analytics, machine learning, or AI, Digital twins or physics-based modeling, wide-bandgap semiconductor technologies. Excellent written and spoken English
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machine learning/artificial intelligence courses and relevant project and thesis experience. You have a keen interest in AI alignment, human-AI interaction, and explainable AI, and enjoy collaborating with
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modeling Demonstrated experience with mathematical modelling, and an ability to learn new concepts and (computational) methods as needed A demonstrated ability to work with programming languages such as R
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work, you will also be expected and encouraged to present your work at relevant workshops and conferences. You will be given every support in these endeavours. Would you like to learn more about what
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or heterogeneous environmental datasets Familiarity with spatial analysis, GIS, or geospatial data workflows. Experience with machine learning, modelling, or systems analysis approaches Interest in resilience
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Research Studentship in Normothermic Machine Perfusion 3.5-year D.Phil. studentship Project: Normothermic Liver and Kidney Perfusion for Organ Preservation, Drug Testing or In Situ Therapies
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cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with ten (10) years of experience; OR MS in the same fields with eight (8) years