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-develop next-generation supply chain analytics leveraging predictive modeling, semantic models, AI, and natural language processing for advanced projects (e.g., semantic item classification, opportunity
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. However, in many real-world and latency-critical applications, performance cannot be assessed solely through final recognition accuracy. Instead, the value of a prediction strongly depends on its timeliness
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through microstructural characterization and comparing experimental observations with thermodynamic model predictions. Publishing your findings in peer-reviewed international journals and presenting
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Application deadline: All year round Research theme: Environmental geochemistry How to apply: https://uom.link/pgr-apply-2425 This 3.5-year PhD studentship is open to EU, UK, and US applicants. The
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broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data
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algorithms for dynamic master selection, coordinating BESS, PV, diesel generators, and other sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python
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modeling, photovoltaics, high-temperature experimentation, and solar energy technologies. Thermophotovoltaic (TPV) systems convert thermal radiation emitted by a hot surface into electricity using lowbandgap
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challenges of learning from network traffic, (ii) train original AI models that are designed to operate precisely on such data, and (iii) demonstrate the viability in production of AI-driven solutions for, e.g
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of Technology, the Netherlands. The mission of the Dynamics and Control Section is to perform research and train next-generation students on the topic of understanding and predicting the dynamics of complex
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: Textual Prediction of Survival (LLM classification & Attention Modelling) This project develops a model to predict patient survival by analyzing heterogeneous clinical documents. Unlike traditional methods