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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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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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education, research, or knowledge-intensive industry. The position reports to the Unit Leader of Colorlab. About the project The research will address the growing need for reliable methods that can assess the
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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
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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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focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal
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: Design, implement and characterize innovative sensing systems based on different sensing principles such as ultrasonic, optical and chemical sensors. Investigate sensor behavior under varying environmental
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, integrate genomics, epitranscriptomics and transcriptomics data, and contribute to the development of novel systems biology and bioinformatics methods for RNA therapeutic target discovery. The project is led