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clustering and CMB lensing power- and bispectra to constrain primordial non-Gaussianity from a joint analysis of SO and LSST. You will work in the research group led by Andrina Nicola and the wider cosmology
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analysis methods using Gaussian processes would be an asset Hold a PhD in astrophysics. Where to apply Website https://www.aplitrak.com/?adid=VUdBLjA3ODA0Ljk5MDhAdWdhLmFwbGl0cmFrLmNvbQ Requirements
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-Gaussian feature distributions as a stepping stone towards the development of generalized auditory profiles. Tabular foundation models may also help to improve the derivation of Common Audiological
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research project (e.g. PhD in physical chemistry, chemical physics, computational chemistry or relevant) and needs to be passionate about research. Experience in usage Gaussian, ADF, Dalton, ORCA etc
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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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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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optimisation, uncertainty/robustness analysis, and surrogate-assisted search (e.g., simple regressors, Gaussian processes, or BO). Prior work with workflow managers (e.g., AiiDA/Airflow/Snakemake) 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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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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relevant) and needs to be passionate about research. Experience in usage Gaussian, ADF, Dalton, ORCA etc. software and parallel computing is required. Skills in data processing and visualizing the results