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impact-based health early warning systems. The successful candidate will join the research team of Dr. Joan Ballester Claramunt (https://www.joanballester.eu/ ) at ISGlobal within the framework
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possible date, preferably by 1st April 2026. This is a full position (100%) limited to 3 years. This PhD position is available within the EU-funded Marie Skłodowska Curie Doctoral Network on Low Data Machine
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funded PhD position on Uncertainty Quantification and Technology Qualification for Advanced Wind Turbine Components. This position is part of the MET2ADAPT Doctoral Network (Meta-Materials and Meta
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-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov random fields, hidden
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Apostolos Voulgarakis (Technical University of Crete), and the attendance in in-person international meetings and training network events. The work will mount on previous work and experience within
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to make decisions for localization, navigation, and cooperation. Within the ERC Starting Grant project CUE-GO – Contextual Radio Cues for Enhancing Decision-Making in Networks of Autonomous Agents
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such as: Advanced transportation systems modeling and simulation that could involve integrated machine learning and network equilibrium/simulation, surrogate models/ reduced order emulators or Bayesian
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, and engagement with emerging technologies and societal needs in areas such as: Advanced transportation systems modeling and simulation that could involve integrated machine learning and network
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public-private partnership conducting phase II trials of new regimens for the treatment of tuberculosis (https://www.unite4tb.org/). Application of Bayesian methods for evidence synthesis for clinical
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. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in R and/or