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calls. New research areas may be added until the application deadline. Beyond the research conducted during the PhD project, a successful candidate is expected to teach three to four hours weekly during
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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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The faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) uses mathematics, electronics and computer technology to contribute to the development of Information and Communication Technology
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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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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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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