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train, validate, and improve model performance. The developed model will be applied to coastal engineering problems, including wave transformation, nearshore circulation, and extreme coastal
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-driven X-ray nanophotonics – i.e., model the physics of free electrons impinging on nanomaterials like van der Waals crystals, and subsequent coherent X-ray emission processes. Lead papers and publish in
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is looking for a Research Fellow to conduct research on AI-enabled discovery and development of advanced functional materials and coating processes, with emphasis on data-driven modelling, uncertainty
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and multi-agent model predictive control algorithms. Familiarity with optimisation techniques, machine-learning methods and data-driven modelling. Familiarity with robotics and autonomous-system
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-based application development). Experience in handling real-world datasets and model development/deployment workflows. Domain expertise in building construction/civil/structural engineering, including
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data-driven modeling. • Experience in model predictive control or reinforcement learning is preferred. • Good publication record in energy-efficient cooling or AI-assisted energy systems is desirable. Req ID: 33224 Apply now
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Job Description Job Alerts Link Apply now Research Fellow (Traffic safety and data analytics) University-Level Unit: College of Design and Engineering Faculty/Department-Level Unit: Civil and
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conditions, which can be used to develop a data-driven model for storm surge prediction. Data Analysis: Process and analyze climate, ocean wave and coastal data to improve model accuracy. Research
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, optoelectronic testing, and machine learning-driven signal reconstruction. Note: This role focuses on device engineering and computation; independent material synthesis is not required. Key Responsibilities
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Engineering invites applications for the position of Research Fellow (LLM for Design-for-Safety). The successful candidate will join the research project “Multimodal LLM-driven Intelligent Design-for-Safety