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for optimisation. (3) Machine Learning-based optimisation: implementation of a preliminary optimisation pipeline (e.g., Bayesian optimisation or reinforcement learning) integrated with the simulator to test
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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Discrete Mathematics Probability and Statistics Regression Analysis Time Series Analysis Bayesian Statistics Mathematical Foundations of Machine Learning Contribute to curriculum development and course
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-country survey datasets for comparative analysis. Conceptualize and refine a theoretical framework integrating intersectionality and stigma processes. Develop and code a Bayesian meta-regression to pool and
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, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators
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specimens to estimate historical age structures over the last 150 years. Forecasting Shifts in the Pollination Service Window. The researcher will use Bayesian inference (e.g., Integrated Nested Laplace
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. et al. (2024). Design of high-performance entangling logic in silicon quantum dot systems with Bayesian optimization. Scientific Reports 14, 10080. https://doi.org/10.1038/s41598-024-60478-9
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at the interface of computational systems biology and mathematics/statistics with a strong attitude to open research software development. For more information visit http://www.fz-juelich.de/ibg/ibg-1/modsim
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nets, stochastic processes, Bayesian networks, etc.), who could integrate well into the laboratory. In coordination with the platforms of the laboratory, the recruited person will be responsible
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, multilevel modeling, data mining, survey methodology and Bayesian influences. (Required) Ability to conduct power calculations to determine sample size needed to answer research questions. (Required