PHD in Risk-Averse Learning and Model Predictive Control for Autonomous Driving University of Leuven, ESAT - STADIUS department, Stadius Center for Dynamic Systems, Signal Processing and Data Analysis

RISK-AVERSE LEARNING AND MODEL PREDICTIVE CONTROL FOR AUTONOMOUS DRIVING

 

(Ref.BAP-2017-695)

Last modified: 11/12/2017

The STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Prof. Panos Patrinos, at the Department of Electrical Engineering (ESAT), KU Leuven is offering a fully funded, 4-year PhD position on the topic of real-time risk-averse learning model predictive control for autonomous vehicles. KU Leuven is among the top European universities (ranked first in Times Higher Education list of most innovative universities in Europe) and a hub for interdisciplinary research in the field of optimization, systems & control. The project will be carried out in collaboration with a leading automotive company.
 

Project

 

The project concerns the development of theory and methodologies combining data-based learning with Model Predictive Control (MPC) in the context of autonomous vehicle motion control. The new methods will be capable of dealing with high-effect low-probability (HELP) events such as unexpected changes in traffic, traction and road conditions and unforeseen movements of other vehicles and pedestrians.To this end, the candidate will build upon the recently developed risk-averse MPC framework within our group, that accounts for the uncertainty within uncertainty estimates, being more resilient than stochastic MPC and less conservative than robust MPC. The overall goal is to develop MPC strategies that are able to learn from data in real time, to continuously improve performance and safety guarantees in the highly uncertain context of autonomous driving will be developed. 

 

Profile

 

Applicants should have a Master's degree from a good-quality university in engineering or a related field. They should possess a strong background and interest in systems & control and, ideally,numerical optimization. They should have well-developed programming and excellent analytical and problem solving skills. Applicants should also have good English communication skills. 
 

Offer

  • A fully funded PhD position for four years at the KU Leuven, one of the top European universities and a hub for interdisciplinary research in the field of optimization, systems & control.
  • KU Leuven offers an attractive working environment, generous remuneration, as well as other employment benefits. 
  • As a PhD student at KU Leuven you have many opportunities to participate at international conferences, research projects and other relevant events, which will extend your professional network and benefit your future career.
 

Interested?

To apply send email to Prof. dr. Panos Patrinos, panos.patrinos@esat.kuleuven.be with subject “PhD application: risk-averse MPC”, attaching an academic CV, a pdf of your diplomas and transcript of course work and grades, a statement of research interests and career goals (1 page max.), sample of technical writing (publication or thesis) and contact details of at least two referees. Deadline: As soon as possible. The position may be closed as soon as a competent candidate has applied.
 

You can apply for this job no later than March 09, 2018 via the online application tool


KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.


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