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of the variability and uncertainty of simulated outputs • an explicit quantification of prediction error • an interpretable and controllable structure (e.g., Gaussian processes, …) 2. Model industrial system
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machine learning and statistics; experience with Gaussian process regression and/or probabilistic regression. Experience with normative modelling is an advantage. Proficiency in Python (and ideally C/C
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and analysis Gaussian processes, random functions, rare events, harmonic analysis Shira Faigenbaum-Golovin Manifold learning, shape space analysis, machine learning, mathematics of data
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emulators of similar datasets (e.g. Gaussian Process methods by the project lead). These results will inform the IPCC AR7, and adaptation and mitigation policymaking by other global stakeholders. You will be
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in physics, chemistry, or related field Basic knowledge of ab-initio methods to address molecular excitations (e.g., Gaussian, NWChem, or any other quantum-chemistry software) Knowledge of quantum
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observational data, and compare the results with those from other emulators of similar datasets (e.g. Gaussian Process methods by the project lead). These results will inform the IPCC AR7, and adaptation and
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emulators of similar datasets (e.g. Gaussian Process methods by the project lead). These results will inform the IPCC AR7, and adaptation and mitigation policymaking by other global stakeholders. You will be
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charge‑transport properties. • Familiarity with codes such as Gaussian, VASP, CRYSTAL, Quantum ESPRESSO, or equivalent. • Ability to collaborate in an interdisciplinary environment and to communicate
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these projections with observational data, and compare the results with those from other emulators of similar datasets (e.g. Gaussian Process methods by the project lead). These results will inform the IPCC AR7, and
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the area of data privacy for dependent data and change-points in Gaussian processes. You publish internationally and give lectures. You apply for projects and raise third-party funds. You hold courses