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Field
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involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim to build more trustworthy and robust AI models that can
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of the position. The successful candidate will have a solid theoretical foundation in one or more of the topics: Computational Mechanics, Finite Element Analysis (FEA), Numerical Optimization
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information theory and quantum foundations, in particular in quantum phase-space methods. • You have a proven experience in numerical methods and programming, particularly the simulation of PDEs
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degree in biomedical engineering, mechanical engineering or related relevant field A demonstrated and substantial experience with computational simulation methods, i.e. finite element analysis English
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, Computational Science, Physics, Engineering, or a closely related discipline Strong background in differential equations and numerical methods Solid programming skills e.g. Python, C++, Julia or similar Interest
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translational medicine, improving diagnostics and healthcare solutions. For more information, please visit our page . Your role We are looking for a highly motivated PhD candidate interested in AI-based methods
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receive intense training on formulating their research question. The courses introduce methods of law and economics research, such as econometrics, game theory, experimental economics, and behavioural law
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context The position is based in the Mechanical Engineering Department and the Roberval Laboratory,within the Numerical methods in mechanics team. The successful candidate will report to the UTC project
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Infrastructure? No Offer Description The PhD candidate will work on the development of advanced statistical and machine learning methods for time series prediction, with applications mainly in the field of traffic
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to use your mathematical skills to model and design optical systems for sustainable high-tech devices for billions of people? Do you like to develop and analyze numerical methods for partial differential