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research with young children Experience with computational methods (e.g., Bayesian modeling, drift diffusion modeling, etc.) Equipment Utilized Physical Demands and Work Environment Overview Statement
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. The project is jointly supervised by Dr. Tarikere Niranjan (https://niranjangroup.weebly.com/prof-tarikere-t-niranjan.html ) and Dr. Emir Efendić (https://eefendic.com ) and examines how decision-makers in
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of their mental models into a machine learning model, using dynamic Bayesian networks to understand, propagate and reduce uncertainty in their assessments. The research will apply models of distributed situation
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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
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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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work closely with the other PhD candidate of PAST, who creates high-resolution proxy-based reconstructions of the same paleoclimate. Together, you apply a Bayesian statistical framework to contrast and
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by combining all available data, taking advantage of the varying temporal resolution and different time spans that the records cover. This work will involve Bayesian tools developed by our research
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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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Singlet and Triplet Excited States, Nature 2022, 609, 502–506. ・Bayesian Molecular Optimization for Accelerating Reverse Intersystem Crossing, Chem. Sci. 2025, 16, 9303–9310. (Scope of change) No change