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quantitative skills (R, Python, or Stata; experience with machine learning or advanced experimental methods is a plus). ▪ Familiarity with VR-related toolkits (e.g., Unity, Unreal, or eye tracking) is an
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sediment management. This will be done for two case studies, namely Western Scheldt and Wadden Sea. To facilitate knowledge exchange and the learning process, interactive workshops will be organized
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higher education teaching and learning. The purpose of the position is to develop the independence as a researcher and to create the opportunity of further development. You will have particular
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robotics engineering. To learn more about the School click here We are currently seeking a Postdoctoral Research Associate in AI Enabled Atomic-Scale Materials Characterisation to work within a highly
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ionic liquids and deep eutectic systems. Analysis of solubility, drug content, stability, and in vitro drug release using HPLC/LC-MS or suitable analytical techniques. Ex vivo permeation studies using
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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. Work on other ATLAS physics topics may also be considered depending on the candidate’s expertise. A PhD in particle physics and substantial experience in data analysis are required. Additional valuable
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, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral, and interpersonal
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. Applicants should have a PhD, or have passed their PhD viva, in plasma science, physics, electrical engineering or a closely related discipline. Essential experience includes previous research on plasma-liquid
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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | 21 days ago
the computational work. Researchers in the group have experience with crystal plasticity using finite element and fast Fourier transform methods, fatigue indicator parameters, process modeling and machine learning