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the integration of high data-density reaction/bioanalysis techniques, organic synthesis, laboratory automation & robotics and machine learning modelling. This exciting project involves the application of innovative
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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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environmental modeling programs to review datasets to ensure compliance with QA/QC criteria, perform statistical analyses, fit model curves to data sets, predict contaminant fate and transport in natural and
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conditions. Experimental data will then be used as key material for long-term durability prediction with micromechanical modelling, along with thermodynamic and reactive transport models. In order to
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the University of Oslo. Place of work is the Department of Mathematics at Blindern, Oslo. Ocean waves follow complex patterns influenced by wind, currents, and the shape of the seafloor. Predicting
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and Simulation Group at ICN2 conducts cutting-edge research in computational materials science, focusing on electronic structure methods, atomistic simulations, and multiscale modelling. The group
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cortex, analyze neuronal population data, and build generative or predictive models to simulate cortical responses. The position requires strong experimental engagement and a collaborative mindset within a
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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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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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