This PhD position is on "Data-Driven Distributional Inference for Reliable Control of Complex Systems" and will focus on control applications with dynamic uncertainty, where lack of knowledge about its model needs to be compensated by scarcely available data.
Decisions under uncertainty are ubiquitous in control engineering and seek to provide quantitative solutions when complexity or lack of knowledge about the underlying systems require the probabilistic modeling of their components. Such random elements are often dynamic, and the designer needs to make inferences about them using a limited amount of data. Furthermore, the data may only reveal partial-state information about the process, which is often corrupted by noise. All these factors hinder the possibility of making accurate inferences about the underlying probabilistic models and their usage for control design. To address these issues, this PhD project will leverage tools from state estimation and uncertainty quantification to fuse information from both the data and the known system dynamics and provide robust uncertainty descriptions for reliable decisions and control.
More information about this position and the application procedure can be found at this link.
For any questions, please contact Dimitris Boskos (d.boskos@tudelft.nl).
