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Field
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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-aware
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validate data-driven methods that combine production data, predictive and state-estimation models, uncertainty-aware learning and constrained optimization to improve process consistency under process drift
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1. A PhD degree in a relevant area, e.g., Chemical Engineering, Control Engineering, Computer Science, Systems Engineering, etc. 2. The candidates should have strong math, modeling, programming, and
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natural environments in the context of cities, megacities and rural areas, with an aim to develop engineering-driven solutions and regulatory strategies for mitigation of environmental risks by means
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theoretical modeling of free-electron-driven X-ray photonics. Working closely with experimentalists, the research fellow will lead the study of coherent X-ray emission from free-electron-driven van der Waals
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, simulation, and experimental validation of energy systems is expected. Preferred Qualifications: Experience with artificial intelligence and machine learning for energy systems, including data-driven modeling
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intelligence methods to complex biological, clinical, or engineering problems. Familiarity with multi-omic data analysis and predictive modeling is highly desirable. Experience with software development, cloud
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to advance nearshore hydrodynamic modelling by integrating data-driven and physics-based approaches for simulating wave propagation, wave transformation, and wave–current interaction from deep water to shallow
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modeling of free-electron-driven EUV nanophotonics. Working closely with experimentalists, the research fellow will lead the study of generating and manipulating EUV radiation on the nanoscale, towards
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1. A Ph.D. degree in Aerospace Engineering, Fluid Mechanics, Applied Mathematics, or a related discipline. 2. Demonstrated expertise in turbulence modelling and compressible