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cosmolgy, galaxy evoltion and stellar astrophysics. Students in my group primarily perform numerical simulations of stars, in order to study broad questions related to the origin of the elements in
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Statistical Methods, Automated Planning and/or Reinforcement Learning.
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I supervise a wide range of projects stellar astronomy. They include modelling stars in 1D or 3D, deciphering the origin of the elements (stellar nucleosynthesis), and observing using optical
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alpha in state x then we end up in a state y which obeys formula psi”, and many more. The most common way to determine the true or false status of a given formula is to use the tableau method [1]. We have
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Bayesian deep learning (e.g., Monte Carlo dropout, deep ensembles, Laplace approximations, and variational inference), several challenges remain: Scalability: Many Bayesian inference methods
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Species’ distributions are shifting in response to global climate change and other human pressures. Accurate methods to monitor and predict distribution shifts are urgently needed to manage
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The United Nations Development Programme has identified access to information as an essential element to support poverty eradication. People living in poverty are often unable to access information
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to learn robotics or human-centered research methods will also be considered. Experience with programming languages (particularly Python), deep learning frameworks, and robotic simulation platforms (ROS
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supergiant stars right before the explosion Searching different astrophysical channels that produce r-process elements Connecting the properties of long-duration gamma-ray bursts and associated supernovae web
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of their remnants (including predictions for GW sources); mixing and transport processes in the stellar interior; nucleosynthesis and the origin of elements, including galacto-chemical evolution - which elements