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.
This position at Politecnico di Milano features a balance of methodology development, algorithm implementation, and realworld applications in the fields of manufacturing and mobile robotics. Its aim is the development of advanced optimal methods based on a hierarchical decomposition of the contreol structure, and the use of embedded learning and optimization at all levels to enable adaptivity and constraint handling. The general methods developed in the project will be tested on experimental test-beds in Milano, as well as in close cooperation with the the other ELO-X PhD fellows, in particularly with those who are based at the ELO-X partners Bosch and Odys, during mutual exchange visits of several months duration.
PhD Project Description
PhD Project: Multilayer MPC for fast embedded implementation:
The next generation of highly automated, interconnected, and collaborative industrial systems will require solutions able to: (a) monitor in real-time the system and components’ behavior to estimate their conditions and spot possible faults or anomalies, and (b) manage many interacting subsystems and processes operating at different timescales, with a hierarchical topology, subject to constraints and performance requirements. These two tasks are strongly connected, sincecondition monitoring and fault detection must be fully integrated in the automation and control system. However, the current design approaches lack a systematic way to carry out such integration. To overcome this challenge, this ESR will develop a design methodology for automation and control systems where learning-based solutions for condition monitoring, fault detection and recovery are implemented at multiple levels and time-scales in complex hierarchical control systems, and seamlessly integrated with optimization-based decision and control algorithms. The idea is to develop a high-level model predictive controller, running at slower frequency, that coordinates plantwide operation and monitor its status using learning-based approaches. At lower layers, local controllers will exploit embedded learning and optimization to regulate single processes, monitor them and learn on-line their performance and conditions, implementing the directives received from the higher layers and sending them a feedback about the local status. Based on the overall feedback received from the lower levels, the high-level controller will re-adjust plant operation and schedule re-configuration and predictive maintenance strategies, with the goal to maximize performance while guaranteeing safety and reliability.
Timeline and remuneration:
The start time can be in May 2021 or in November 2021. The PhD projects last for the duration of three years, and are carried out at the 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 fellows. The first year is mainly dedicated to studying and getting acquainted with the relevant state of the art in optimization, model predictive control, machine learning, and recurrent neural networks. The second year focuses on method development, and the third year on application problems, publications, and thesis preparation. 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 2400-3000 Euro depending on family status and mobility.
Ideal candidates have a master degree in one of the following disciplines or a related field: industrial engineering (e.g. control, mechanical, electronic, aerospace), mathematics, or computer science. They should have a good background or interest in mathematical optimization, dynamic system modelling and simulation, 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.Ideal candidates have a master degree in one of the following disciplines or a related field: industrial engineering (e.g. control, mechanical, electronic, aerospace), mathematics, computer science. They should have a good background or interest in mathematical optimization, dynamic system modelling and simulation, 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
Supervisor at the Politecnico di Milano:
Prof. Lorenzo Fagiano (professor of Automation and Control Engineering);
Main Contacts at the ELO-X Partner Groups which could host secondments:
Bosch GmbH: Dr. Stefan Gering;
ODYS Srl: Prof. Alberto Bemporad;
University of Freiburg: Prof. Moritz Diehl.
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 7”.
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 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:
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