The PhD position is part of the European Training Network ‟ELO-X – Embedded Learning and Optimization for the neXt generation of smart industrial control systems”. ELO-X will recruit altogether 15 PhD fellows at 6 research universities and 5 international companies from 5 European countries, who will meet regularly during exchange visits, training events, workshops, and summer schools organized by the network.
The position at KU Leuven has a strong methodological focus in the field of computational control and mathematical optimization. It is based in the STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, in the group headed by Prof. Panos Patrinos. Its aim is the development of learning-based, interaction-aware model predictive control methodologies and embedded optimization algorithms for autonomous systems. While these methods are generic and applicable in several branches of engineering, they shall be tested and used in close cooperation with the the other ELO-X PhD fellows, in particularly with those who are based in Siemens Industry Software NV and Robert Bosch GmbH – during mutual exchange visits of several months duration.
PhD Project Description
PhD Position in Embedded Learning and Optimization for Interaction-aware MPC:
Autonomous navigation in uncertain environments, where vehicles (autonomous cars, robots, drones) must interact with other vehicles or other users (e.g., pedestrians) is a highly challenging control task. Thus, the development of real-time MPC strategies that take into account interaction and uncertainty is highly desirable. Traditional means of handling uncertainty such as robust or stochastic approaches are either too conservative or too risk-prone. Moreover, MPC methodologies typically assume that the uncertain behavior of surrounding users is completely exogenous, thus failing to take interaction into account. In order to explicitly take into account interactions, the aim of this PhD project is to develop stochastic models for the surrounding users whose probabilistic structure depends on the states and actions of other users. The transition probabilities are learned online using machine learning in a moving horizon fashion in order to account for dynamic environments. Furthermore, the proposed methodology will provide a natural mechanism for the real-time learning system to safely balance exploitation and exploration, as the optimal input will be exploratory (that is, associated with more uncertain state transitions) only if the risk is sufficiently low. The resulting NMPC formulations will lead optimal control problems that are more complex and of larger scale than what state-of-the-art embedded solvers can handle. The goal of this project is to develop embedded optimization and online learning algorithms for interaction-aware MPC for autonomous navigation in uncertain environments.
Timeline and remuneration:
The ideal start time is in spring or summer/early autumn 2021. The PhD project lasts for the duration of three years, and is carried out at KU Leuven. The PhD years include at least one longer visit – a so called ”secondment” – between one and six months to another group in the ELO-X network, depending on the project needs and the scientific interests of the PhD fellows. The first year is mainly dedicated to studying and getting acquainted with the relevant state of the art in learning-based control and numerical optimization, the second year focuses on method development, and the third year on application problems and publications. A fourth PhD year can be added and funds are reserved for this at KU Leuven. The remuneration is generous and will be in line with the EC rules for Marie Curie grant holders. It consists of a salary augmented by a mobility allowance, resulting in a net monthly salary of about 2100-2200 Euro depending on your personal situation. If at the time of recruitment you are married or in a legally recognized equivalent relationship and/or you have dependent children, you are also entitled to a monthly family allowance. The exact amount will be confirmed when your application file is complete.
Ideal candidates have a master degree in one of the following disciplines or a related field: mathematical, electrical or mechanical engineering or compute science, numerical mathematics. They should have a good background or interest in mathematical optimization, system & control, machine learning, and programming (Matlab, C/C++, python), as well as a desire to contribute to the development of open-source software and the success of real-world experiments. Proficiency in English is a requirement. The positions adhere to the European policy of balanced ethnicity, age and gender. Both men and women are encouraged to apply.
Supervisors and Main Contacts
Supervising team at the KU Leuven:
Prof. Panos Patrinos
Mathijs Schuurmans and Peter Coppens (PhD students working on safe learning-based control, risk-averse and distributionally robust model predictive control)
Main Contacts at the ELO-X Partner Groups which could host secondments:
Stanford University: Prof. Stephen Boyd;
Siemens Industry Software NV: Dr. H. Van der Auweraer, Dr. Son Tong;
Robert Bosch GmbH: Maximilian Manderla, Stefan Gering.
To apply, send an email to email@example.com in form of one single PDF attachment containing all contents or links (any other information within the email will not be processed). Subject of your email should be: “ELO-X PhD Application – ESR 4”.
Please include, in your single PDF document, the following items in this order:
A cover letter incl. statement of research interests and career goals (max. 2 pages);
An academic CV;
Contact details of at least two referees incl. phone numbers and emails;
Your diplomas and transcript of course work and grades;
Sample of technical writing (publication or thesis);
Proof of English language proficiency test results.
Please send your application before March 25, 2021.
Note that your PDF will be forwarded to several people in the ELO-X institutions and that in particular all Supervisory Board members of ELO-X will have access to your application material. If you want to apply to more than one ELO-X position, please create and send separate PDFs.
Marie Curie Fellowship Eligibility Criteria in Short
To be eligible, you need to be an “Early Stage Researcher” (ESR) i.e. simultaneously fulfill the following criteria at the time of recruitment:
Nationality: you may be of any nationality.
Mobility: you must not have resided or carried out your main activity (work, studies, etc…) in Belgium for more than 12 months in the 3 years immediately prior to your recruitment under the ELO-X project.
Qualifications and research experience: you must be in the first 4 years of your research career after the master degree was awarded.
Embedded learning and optimization for the next generation of smart industrial control systems European Training Network (ETN)
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.953348
Project coordinated by:
Department of Microsystems Engineering (IMTEK)
University of Freiburg, Germany