PhD Scholarship to The Machine Learning for Smart Mobility Group

Technical University of Denmark, Denmark is inviting excellent students to apply for a PhD position in artificial intelligence. The deadline to apply is September 10, 2019.

Shared, connected and cooperative automated vehicles may become a game changer for urban mobility. They can provide seamless door to door mobility of people & freight delivery services, which can lead to healthier, more accessible, greener & more sustainable cities, as long as they are integrated in an effective PT system. Since a few years the development of shared automated vehicle pilots are emerging around the world with the promise of great potential impacts on reducing CO2 emissions and pollutants, improving safety and reducing overall transport system costs. However, in order to be attractive and accepted by users, these systems and services need to be, among others, efficient, adaptive to user demand and robust to disruptions (e.g. caused by special events, inclement weather, incidents and road works). Fortunately, shared, connected and cooperative automated vehicles also constitute a unique Big Data source. This data, when coupled with the latest developments in Machine Learning and Artificial Intelligence in general, enables the implementation of smart and safe mobility solutions, innovative traveller services and (city) traffic management.

The focus of this research is thus on leveraging state-of-the-art Machine Learning techniques to develop and implement novel solutions for improving the effectiveness, adaptivity and robustness of shared, connected and cooperative automated vehicles. This may include, for example, the development of: shared mobility demand prediction models accounting for the impact of external factors (e.g. special events, inclement weather, incidents and road works), robust and reliable arrival-time / travel-time prediction systems that are able to provide uncertainty estimates of their predictions, predictive routing approaches for a fleet of autonomous vehicles for optimally adapting supply to demand while also accounting for demand uncertainty, etc.

Overall, this research thus lies in the intersection between Machine Learning (namely Deep Learning, Probabilistic Modelling and Bayesian Statistics) and Transportation Systems. The Machine Learning for Smart Mobility group of the Transport Division of the Department of Management at the Technical University of Denmark (DTU) is looking for excellent applicants to join the Division, starting on December 2019 or earlier. We are looking for excellent applicants with MSc background on Computer Science, Transportation, Applied Mathematics and Statistics or related, and with the interest and ambition to pursue PhD studies.

Deadline: September 10, 2019
Apply Link:http://tinyurl.com/y2ephdr3

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