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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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-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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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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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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global network of campuses and partners for students and faculty to leverage for learning and research; a deep investment in lifelong and experiential learning; a premium placed on pedagogical innovation
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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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induction, nearest neighbour classification, Bayesian learning, neural networks, association rules, and clustering are explored. The course also addresses approaches for handling unstructured data, including
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significant external research funding. Experience supervising doctoral or postdoctoral researchers. Expertise in Bayesian and/or adaptive trial designs and dose-finding methodologies. Strong leadership and team