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 Siemens Industries Software (SISW) has strong methodological and application focuses in the field of advanced driver assistance systems (ADAS) and autonomous driving (AD).
It is based in the R&D ADAS team, headed by Dr. Son Tong and Dr. Herman Van der Auweraer.
Their aim is the development of advanced planning and control technologies dealing with both safety
and comfort driving. The algorithm developments will be in close cooperation with the the other ELO-X PhD fellows, in particularly with those who are based in KULeuven, EPFL, and UPB during mutual exchange visits of several months duration.
Digital technologies are transforming all sectors of our economy and will increasingly do so in the years to come. Thanks to the increasing capabilities of digital technologies, the next generation of smart industrial control systems (SICS) are expected to learn from streams of data and to take optimal decisions in real-time on the process at hand, leading to increased performance, safety, energy
efficiency, and ultimately value creation. Numerical optimization is at the very core of both learning and decision-making, since both the extraction of information from data and the choice of the most suitable action are naturally cast as optimization problems and solved numerically. However, to realize this potential embedded learning and optimization methods needs to be developed, able to operate in
industrial devices and to guarantee high safety standards. ELO-X addresses the timely and pressing need for highly qualified and competent researchers, able to develop embedded learning- and optimization-based control methodologies for SICS, thus enabling new technologies and the next generation of digital industrial products and processes.
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 efficently 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 reserach programs, and have close collaborations with academic institutes.
The ELO-X PhD positions 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
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.
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, (Optimization, Systems and Control, ESAT-STADIUS); EPFL: Prof. Colin Jones (Associate Professor, Head of Automatic Control Laboratory); UPB:
Prof. Ion Necoara (Automatic Control and Systems Engineering Departament).
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
MARIE CURIE ELIGIBILITY CRITERIA IN SHORT
To be eligible, you need to be an "early stage researcher" i.e. simultaneously fulfill the following criteria at the time of recruitment:
a) Nationality: you may be of any nationality.
b) 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.
c) Qualifications and research experience: you must be in the first 4 years of your research career after the master degree was awarded.
Organization: Digital Industries
Company: Siemens Industry Software NV
Experience Level: Early Professional
Job Type: Full-time