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epidemiology, and working with methods like random forest and targeted learning, the candidate will contribute to the development of interpretable survival models and doubly robust estimation methods
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circuit models and algorithms for estimating the charge, health, and power based on direct methods (e.g. open circuit voltage), model-based methods (e.g. Kalman filtering), data driven methods (e.g. machine
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models of steel structures with emphasis on fatigue hot-spot modelling, building on existing in-house methods Load and stress estimation using virtual sensing techniques (e.g., Kalman Filters) combined
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be to develop wireless sensing and communication methods that are designed together with AI-based inference, rather than treating connectivity as a separate layer. Particular attention will be given
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of Engineering programme as Associate Professor in AI and Computer Vision. You will play a central role in educating future engineers, combining deep technical expertise with close interaction with students in a
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that explain how humans learn, adapt and stabilise navigation behaviour in urban environments. The project will combine methods from transportation science, artificial intelligence, computational neuroscience
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already have used data-driven computational methods to model cognitive or behavioural change in any substantive domain, that would be ideal. Experience specifically with research on consumers or citizens
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computer vision concepts and methods and can combine these with data-driven approaches when relevant. Experience with machine learning operations, such as model deployment, experiment tracking or scalable
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models of steel structures with emphasis on fatigue hot-spot modelling, building on existing in-house methods Load and stress estimation using virtual sensing techniques (e.g., Kalman Filters) combined
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competence You have Completed an academic degree at the candidate or master level in a STEM discipline (e. g., Chemistry, Biology, Physics, Computer Science, Human-Computer Interaction, IT Product Development