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models to analyze and mitigate fine particulate matter (PM2.5) exposure from various infrastructure systems (e.g., transportation networks, manufacturing systems, and truck routing). Assessing
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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interaction, adapt to new tasks, and generalize across environments. Dexterous Manipulation – Advancing robotic grasping, in-hand manipulation, and adaptable object handling in complex real-world scenarios
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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large
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Post-Doctoral Associate in the Division of Social Science - Dr. Morgan Hardy and Dr. Veda Narasimhan
change, and migration often involving fieldwork in various African and Asian contexts. We seek a candidate with expertise in managing complex fieldwork on the African and Asian continents, including survey
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responsible for the project Design, synthesis, and characterization of complex organic molecules which play a pivotal role in DNP, Employ advanced NMR/MRI methods for structural analysis and DNP-related studies
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/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and
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complex sociotechnical systems Strategic learning and equilibrium-seeking algorithms in transportation networks Game-theoretic approaches to cybersecurity and security games Integration of human behavior
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inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic
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systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design