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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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observations (DR1), based on this detection method. The candidate will derive cosmological constraints from the modelling of the cluster abundance, using the classical Bayesian framework, and will also
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models, spatial Bayesian methods, case time series, case crossover. Have experience with the management and analysis of large climate and/or health databases. Have experience with Linux environment and
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isotopes, TIMS and ICP-MS. B3 Knowledge and experience of project-specific technical models (e.g., Bayesian modelling), equipment or techniques including high-precision CA-ID-TIMS and LA-ICPMS U-Pb
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circuit mechanism underlying higher cognitive functions such as multitasking, rule-based reasoning and Bayesian inference). In addition to the above areas, there is extensive expertise available in
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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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-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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datasets. Proficiency with geometric morphometrics and image alignment. Proficiency in applying quantitative genetic methods to large datasets. Proficiency with large-scale animal models using Bayesian
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equilibrium/simulation, surrogate models/ reduced order emulators or Bayesian or interpretable machine learning. Simulation and optimization of on-demand transportation services or novel transit systems and
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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