-
and implementation of incentive mechanisms for sociotechnical and cyber-physical-human systems, with particular emphasis on smart mobility and urban transportation networks. In particular
-
the study of the development of singularities, the long-time behavior of complex systems, the formation of spatial patterns, the transition between stability and instability, and the first steps of transition
-
: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design Creating and validating digital twin architectures that incorporate physical laws and constraints
-
implement innovative solutions. He/she will contribute to the development of novel concepts and proposal writing, while efficiently addressing complex challenges. Responsibilities will include writing reports
-
networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
-
and implementation of incentive mechanisms for sociotechnical and cyber-physical-human systems, with particular emphasis on smart mobility and urban transportation networks. In particular
-
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
-
: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design Creating and validating digital twin architectures that incorporate physical laws and constraints
-
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
-
hypothesis generation in finance / natural sciences / physical sciences, enhancing collaborative workflows in complex organizational settings. Qualifications: Applicants must have a PhD in Computer