PhD position in Statistics and AI for quantitative crowd dynamics modeling

The Department of Mathematics and Computer Science of the Eindhoven University of Technology is searching for an excellent candidate for a 4-year Ph.D. position for the project “AICrowd: AI-Based Pedestrian Crowd Modelling and Management”. Are you eager to work within a pioneering project at the interface between mathematical statistics, artificial intelligence, physics of flowing matter and system identification? Do you enjoy collaborating with researchers from different fields? Then this position might be for you.

Job description: PhD position in Statistics and AI for quantitative crowd dynamics modeling

Whenever our safety and comfort in public areas are at risk because of dense crowds, crowd management failed. Even quite recently these dysfunctions have cascaded into unacceptable catastrophic events. This PhD position is part of the project AICrowd: AI-Based Pedestrian Crowd Modelling and Management, aimed at significantly improving the quantitative modelling of human crowd behavior, and fueled in part by having access to a massive amount of individual pedestrian data obtained through extensive (past an ongoing) experimental campaigns. The project involves two PhD candidates and encompasses three outstanding challenges: quantitative stochastic modeling of crowds, scalable model learning, and optimal actuation for experimental design and control.

In this position you will work in the team of Rui Castro (Mathematics/Mathematical Statistics) and be co-supervised by Alessandro Corbetta (Applied Physics/Fluids and Flows) and Maarten Schoukens (Electrical Engineering/Control Systems). Within the AICrowd project there will be two PhD students that will collaborate actively in the research efforts, but each with a different focus culminating in two separate PhD theses.

A substantial part of your research work will be focused both on the development of sound methods for sequential identification of informative data, leading efficient learning of crowd dynamic models, as well as the development of the foundations of adaptive sensing in the general context of system identification. You will be embedded in the Statistics, Probability and Operations Research (SPOR) cluster of the mathematics department and collaborate closely with experts from the Applied Physics and Electrical Engineering departments, among others.

Job requirements:

Candidates should hold a master’s degree (or an equivalent university degree) in mathematics, applied mathematics or mathematical statistics. Candidates with a different background might be considered if they can demonstrate they have very strong mathematical foundations. Successful applicants must showcase a strong affinity for formal mathematical reasoning, especially in the context of statistics, probability, and machine learning. Given the interdisciplinarity of this project, candidates must have good organization and communication skills, be self-motivated and independent, and have affinity with complex programming efforts. Research experience will be highly valued. Proficiency in English is also required - if deemed needed the candidate will be requested to supply the necessary evidence.

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