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modeling, computer simulation, non-linear model analysis, interactive learning environments and decision-laboratory experiments. About the project/work tasks: Description of the INTEGRATOR project
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operational interdependencies. Investigate approaches for defining adaptive or virtual subnetworks that complement existing District Metered Areas. Develop methods for semi-autonomous local decision-making
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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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, these methods can be subjective, time-consuming and difficult to scale. Meanwhile, mental health states may also be reflected in how people express, perceive and respond to the world through facial behaviour
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various relevant computer software applications. • Assist with literature research, editing and preparing manuscripts, scientific illustrations and computer graphics for publication. • May perform
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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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rigorous checks, coding, data cleaning, and privacy protection. Web Surveys : Oversee the programming of online platforms such as Qualtrics, managing access, and monitor completion rates. Computer-Assisted
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states and inputs, such as path flows, boundary conditions, and other dynamic network variables. You will: Develop new data assimilation methods for estimating traffic states and network conditions Combine
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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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, provide only limited insight into how such interference behaves at system level. In this PhD project, you will develop the measurement and analysis methods needed to make these environments observable. You