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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material
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these changes affect ecosystem functions. To extend these analyses to new types of data and questions, we develop state-of-the-art hierarchical Bayesian methodology. We also actively apply our research to more
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. Additional qualifications: It is advantageous to have experience in one or more of the following areas: Machine Learning & Bayesian optimization (Python, Supervised learning, Multi-objective optimization
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to have experience in one or more of the following areas: Machine Learning & Bayesian optimization (Python, Supervised learning, Multi-objective optimization) Additive manufacturing of metals Corrosion
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health. You will develop and apply cutting-edge machine-learning techniques to identify the most informative indicators of ecosystem change and use them to build dynamic Bayesian network (DBN) ecosystem
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tracking and autoencoders for amplitude and phase noise characterization Bayesian filtering Building experimental set-ups for noise characterization Reinforcement learning strategies for comb generation
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focus on the following areas: Subspace tracking and autoencoders for amplitude and phase noise characterization Bayesian filtering Building experimental set-ups for noise characterization Reinforcement
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learning, evidence synthesis in public health and statistical genetics and genomics. We are recognised for our strength in Bayesian inference applied to biomedicine and public health. The MRC Biostatistics
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to improve R&D efficiency, and the influence of investors and other external actors on entrepreneurial outcomes. Our research also examines decision-making under uncertainty, including the use of Bayesian
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the Netherlands’ national bird, the black-tailed godwit. Join our team! We seek a quantitative, ecology-minded PhD candidate to expand our state-of-the-art Bayesian Integrated Population Model (IPM