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- Delft University of Technology (TU Delft)
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- Delft University of Technology (TU Delft); today published
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Bayes factor hypothesis tests in factorial designs. What are you going to do The envisioned projects will focus on the following activities related to Bayesian inference in factorial designs: Construction
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on the following activities related to Bayesian inference in factorial designs: Construction and elicitation of informed prior distributions; Critical assessment of default prior distributions; Organizing a many
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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
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Linguistics and Psycholinguistics: Development and application of statistical machine learning, (hierarchical) Bayesian modelling, as well as advanced deep neural models for processing linguistic and visual
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for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice
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of statistical machine learning, (hierarchical) Bayesian modelling, as well as advanced deep neural models for processing linguistic and visual data in humans and machines; and Computational Models of Brain and
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modelling approach, and dynamic Bayesian Networks would be advantageous. Willingness to conduct research in a multi-national project team. Funding requirements: You cannot have resided in The Netherlands in
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with process safety and security concepts, accident modelling approach, and dynamic Bayesian Networks would be advantageous. Willingness to conduct research in a multi-national project team. Funding
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) for engineering systems and structures, as well as expertise in machine learning, stochastic modeling, and Bayesian statistics. Programming Skills: Proficiency in programming languages such as Python, C, or R