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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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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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; maintain version control and prepares data for submission to public repositories and collaborative networks. Conduct statistical and spatial analyses of ecological and climate datasets. o Implement
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high-dimensional neural data. Approaches used include neural network-based approaches, Bayesian inference, and more Assisting with the oversight of day-to-day functions of the lab and shared lab spaces
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national and international research networks as well as pursue independent and collaborative methodological research in Biostatistics. Methodological interest and experience are desired, but not limited