Embedded learning and optimization for the next generation of smart industrial control systems

ESR12 – Siemens Industries Software

PhD Position in Embedded Learning and Optimization for Autonomous Vehicle Control




Siemens Industries Software, Belgium



  1. Introduction
  2. PhD Project Descriptions
  3. Supervisors and Main Contacts
  4. Candidate Profiles
  5. Application
  6. Marie Curie Eligibility Criteria In Short




As a global industry leader, Siemens has a clear focus on innovation. Siemens delivers pioneering technologies that will radically change mobility in the near future, enabling electrification, autonomous driving, smart cities and more. SISW solutions portfolio provides a digital twin approach from chip to city to bring complex, smart products to market faster and with greater confidence. To optimize the safety and comfort performance of autonomous vehicles, Siemens promotes a closed-loop vehicle development process that consumes recorded data during the lifecycle of the vehicle to drive improvements in the design of the vehicle and its controllers. The R&D ADAS team in Leuven focuses on autonomous driving toolchain development, from perception to planning and control. The potential R&D results are usually exploited toward commercial solutions. Methodologies to balance safety and comfort (or human-like) driving efficiently are one of our main topics recently. The company industry-standard testing facilities allow the fellows to validate their algorithms efficiently, in both virtual and real environment. Moreover, we are actively involve in different research programs, and have close collaborations with academic institute’s. This ELO-X PhD position will be supervised by experts and experienced research engineers in control systems, autonomous driving, vehicle dynamics and shall prepare the fellows for a high-level career in automotive industry.

PhD Project Description
PhD Project: Optimal planning and control algorithms for autonomous driving:
The aim of this PhD position is to develop advanced optimization and machine learning methods that are able to address challenging safety and comfort problems in autonomous driving.
Safety is considered as the main driver for AD and ADAS development. While several existing assistance functionalities have proven their capabilities in simple use cases (e.g. adaptive cruise control), the automotive industry is continuously dealing with more safetycritical scenarios such as emergency lane change and intersection crossing. Today, most common control designs in the automotive industry rely on model-based non-optimal methods. They sometimes struggle to deal with safety-critical scenarios. Comfort, or the occupant’s perception of the vehicle performance in ADAS scenarios, is another challenge for automotive OEMs. Customers will only accept the ADAS functions if they experience comfortable feelings, and do not urge to take over vehicle control. Though significant knowledge is available on the performance perception for human drivers, these previous studies are no longer applicable for ADAS scenarios where the focus is on the occupant. Moreover, comfort objectives are usually hard to be considered during the design of safetybased controller. Recently, machine learning techniques have shown advantages in several AD control applications. Methods such as imitation learning can introduce human likeness in the world of controls. However, they show limitations due to their lack of fundamental and rigorous results on explainability, safety and stability. Therefore, a novel methodogical and embedded implementation development, which combines both learning human-like behaviour and safety objectives to increase the AD acceptability is the main focus in this PhD project.

Timeline and remuneration:
The ideal start time is in spring or early summer 2021. The PhD project last for the duration of three years, and is carried out at Siemens Industries Software, Leuven, Belgium. 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 optimal and learning control, 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. 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 1900-2300 Euro depending on family status.
Candidate Profiles
Ideal candidates have a master degree in one of the following disciplines or a related field: control systems, automotive engineering, robotics, or machine learning. They should have a good background or interest in autonomous driving, simulation, optimization, and programming (Matlab, C/C++, Python, ROS), as well as a desire to work on physical vehicle testing. 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 Siemens Industries Software:
  • Dr. Son Tong (Senior Researcher);
  • Dr. Herman Van der Auweraer (R&D Director);
Main Contacts at the ELO-X Partner Groups which could host secondments:
  • KULeuven: Prof. Panagiotis Patrinos;
  • EPFL: Prof. Colin Jones;
  • UPB: Prof. Ion Necoara.
To apply, send an email to elo-x@imtek.uni-freiburg.de 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 12”.

Please include, in your single PDF document, the following items in this order:

  1. A cover letter incl. statement of research interests and career goals (max. 2 pages);
  2. An academic CV;
  3. Contact details of at least two referees incl. phone numbers and emails;
  4. Your diplomas and transcript of course work and grades;
  5. Sample of technical writing (publication or thesis);
  6. Proof of English language proficiency test results.


Please send your application before January 17, 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:
  1. Nationality: you may be of any nationality.
  2. 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.
  3. Qualifications and research experience: you must be in the first 4 years of your research career after the master degree was awarded.