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are looking for candidates interested in developing new machine learning methods for medical image analysis, with a particular focus in anomaly detection and unsupervised learning. In this position, you will
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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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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
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experimental methods in thermal and fluid mechanics, including data analysis, uncertainty estimation, and model validation, is desirable. Familiarity with optical measurement techniques in fluid mechanics and
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-edge fabrication capabilities. Evaluate new process requirements against current capabilities to perform risk assessments and provide accurate yield estimations. Qualifications Masters/Ph. D in material
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Applications are invited for a 24-month research assistant position in the field of Aerial Robots at the Department of Electrical and Computer Engineering, Aarhus University, Denmark. Expected start