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electronic test and measurement equipment, investigation of transient electrical behaviour, comparison of different device designs and fabrication variants, and support for the evaluation of device performance
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estimated from observed data. The overall aim of the project is to develop statistical theory, methodology, and computational methods for such complex data problems, with a particular focus on models
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methods developed in our laboratory by combining fluorescent viscosity-sensitive probes and confocal/infrared microscopy with mechanical measurements to correlate the microscale of pore interaction
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interest are not directly observable. These may include, for example, ability, attitudes, well-being, or different dimensions of poverty, which instead must be estimated from observed data. The overall aim
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uncertainty, detect errors, and regulate their behavior accordingly. The PhD project will examine how metacognitive monitoring and regulation develop across different age groups and within individuals over time
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to players’ needs in real time. The successful candidate will work with an interdisciplinary supervisory team, benefiting from expertise in adaptive systems, accessibility and human-computer interaction, and
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develop quantitative methods to estimate effects on infrastructure degradation, maintenance needs, operational risk, punctuality and costs. The aim is to develop and validate a practical, transparent
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other components. The PhD candidate will link TrainGate detections and early warnings with maintenance, incident, operational and cost data and develop quantitative methods to estimate effects
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the short-term load limits for transformers will free up capacity to accommodate new load and generation, reduce the need for reinvestments, and provide back-up capacity in case of emergency. Improved methods
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ingredients, a process that is traditionally slow because each substrate–strain combination behaves differently. By applying machine learning to historical experimental data, we can predict high‑potential