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 candidate will operate at ODYS premises in Milano, Italy, and will be enrolled as a PhD student of Politecnico di Milano. The research activity will involve both methodological and application aspects in the field of embedded control, machine learning, and numerical optimization. The aim of the research is to develop advanced self-calibrating nonlinear model predictive control (MPC) methods, their software implementation, and their application to industrially relevant control problems with focus on the automotive domain. The research will be carried out in close cooperation with European leading experts in embedded optimization and MPC, in particular with Politecnico di Milano, K.U.Leuven, and Politehnica University of Bucharest, during mutual exchange visits of several months duration.
ODYS S.r.l. is a private SME specialized in developing MPC systems for next-gen controls in industrial production. ODYS expertise stems from more than 25 years of scientific research and development of advanced multivariable control methods and software, efficient real-time optimization algorithms, and tools for their deployment in production. ODYS is a supplier of production-oriented MPC software and related consultancy services for major OEMs worldwide.
PhD Project Description
PhD Project: Embedded model predictive control of automotive systems with self-calibration.
Next-generation embedded control systems in vehicles will have to cope with tight constraints on computation and storage resources, while at the same time satisfying increasingly challenging requirements on performance quality, energy consumption, safety, emissions, etc. In this context, model-based control techniques such as MPC are envisioned to play a key role, by providing a systematic framework to minimize a desired objective function under constraints. Real application of online MPC to high-volume automotive production programs has only very recently started to blossom and will likely continue to spread in the next years. For automotive suppliers of services and software like ODYS, it is essential to devote relevant resources to ease the design of MPC controllers and improve their performance, by reducing calibration time before production and adapting calibration parameters at run-time. To achieve these goals, the following three elements are equally important and demanding in terms of resources: (1) on-line adaptation of prediction models to cope with part-to-part variations, system aging and wear and tear of components, and to diagnose malfunctions of sensors and actuators, (2) use of reinforcement learning and policy search methods for self-tuning of MPC controllers, (3) a solution algorithm for the resulting optimization problem. Being able to perform the above three operations efficiently, and in a way that they are strictly intertwined, is key to enable the next-generation MPC modules in industrial production programs in the automotive industry. The ESR will address several aspects of the above mentioned challenges, pursuing both theoretical research and application of the research results to embedded industrial use cases.
The expected outcomes of the research project are: (1) novel techniques for adaptive nonlinear MPC based on online machine learning, (2) control-oriented sparse optimization that allows one to trade off accuracy of the solution with speed of computation and memory occupancy, (3) tailored software implementation for highly resource-constrained embedded devices.
Timeline and remuneration:
The ideal start time is in Fall 2021. The PhD project lasts for the duration of three years, and is carried out at ODYS. The candidate will also be enrolled as a PhD student of Politecnico di Milano. 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 candidate. The first year is mainly dedicated to studying and getting acquainted with the relevant state of the art in MPC, machine learning, and embedded optimization, the second year focuses on method development, and the third year on application problems and publications. 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 gross monthly salary of about 4000-4200 Euro depending on family status.
Supervisors and Main Contacts
Supervising team at ODYS:
Dr. Daniele Bernardini (co-founder and CTO of ODYS)
Dr. Gionata Cimini (Technical Lead for embedded optimization).
Main Contacts at the ELO-X Partner Groups which could host secondments:
Politecnico di Milano: prof. Lorenzo Fagiano;
KU Leuven: prof. Panagiotis Patrinos;
Politehnica University of Bucharest: prof. Ion Necoara.
Ideal candidates have a master degree in one of the following disciplines or a related field: control engineering, computer science, or applied mathematics. They should have a good background or interest in control engineering, mathematical optimization, machine learning, dynamic system modelling and simulation, and programming (Matlab, C/C++, python), and a desire to contribute to the development and the success of real-world applications. 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.
To apply, send an email to firstname.lastname@example.org 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 14”.
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 February 28, 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” 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 Italy 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