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models and Bayesian approaches to tackle complex, real-world data? Join this PhD project to build dynamic models and study cognitive variability using ecological momentary assessment (EMA). Join us We are
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optimization-based updates (e.g., stochastic gradient methods and Bayesian learning), Probabilistic performance guarantees, leveraging tools from stochastic systems, RKHS-based learning, and Bayesian inference
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. mixed effects regression models and/or Bayesian statistics; e.g., brms / lme4 packages). Excellent written and spoken English. Desirables (traits that would give you an advantage) Training in evolutionary
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the necessary methods to study how smart AI-models are compared to people, now and in the future, and sheds light on how to safely utilize AI in our daily lives. The main objective of this PhD project is to
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embedded in the CCSS community. Nice to have Experience with viral genomics, phylogenetics, Bayesian or likelihood-based inference, infectious disease modeling, or high-performance computing. Additional
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programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. In particular, you will be part of the Causality team under the supervision
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are focused on areas with high noise exposure: areas near the runway or final approach or early departure routes. Current noise models only consider a free propagation path from the sound source towards
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an advantage. For PhD 2, demonstrable expertise in modal logic (and in particular predicate approaches to modality) will be an advantage. Your track-record is that of a talented researcher ready to contribute
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description This project addresses the effective design of a military supply logistics network, composed of transportation and communication links such as roads and rail, aerial drone routes, and nodes, such as
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of microscopic objects Job description Ultrafast photoacoustics is a process in which we use extremely short laser pulses to heat a material, giving rise to a rapid expansion that results in the generation of high