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exchangers (HX) for optimizing thermal storage in silos. The researcher will deploy conjugate heat transfer CFD models and conduct simulations to provide high-fidelity predictions of available power, charging
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learning, generalization/robustness and privacy aspects in scalable learning algorithms. Large‑scale optimization and control: Optimal control, model predictive control and other optimization‑based control
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project activities, as predicted in the application: Activity 2. TRL 5 cultivation and AI model development Activity 3. Biomass analysis and process techno-economic and sustainability assessment
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stars and potentially very massive stars (> 100 Msun). Observations will be compared with predictions from synthetic population synthesis models accounting for single and binary evolution, plus various
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on the project can be found here: https://hecustom.eu/ This post will contribute to the creation and validation of a digital twin (with biological bone models) to assess and interrogate the issue of
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& Budget Strategy Institutional Analytics & Decision Support Policy, Planning & State Operations University Business Services The Modeler supports the University’s strategic, financial, and operational
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, diesel generators, and other sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python, or embedded software tools. Integrate controller logic with
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Efficient Quantum Architectures. arXiv preprint arXiv:2508.05339. Nugraha, F. P. and Shao, Q. (2023). Machine Learning-Based Predictive Modeling for Designing Transmon Superconducting Qubits, 2023 IEEE
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analytical tools, coupled with advanced crop modelling techniques so to evaluate the effectiveness and predict outcomes of various crop production strategies. To develop a strategic vision of the agricultural
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deep learning models (e.g., adapting methods in [6]) based on spatial cellular graphs constructed from these images to predict clinical outcomes. The research will be carried out using two