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 has a strong methodological focus in the field of machine learning and artificial intelligence for control. Its aim is the development of advanced optimal control methods based on machine learning techniques and opensource software and their application to industrially relevant optimization and estimation problems. While these methods are general and applicable in several branches of engineering, they shall be tested in close cooperation with the the other ELO-X PhD fellows, in particularly with those who are based at Tool Temp and Freiburg University, during mutual exchange visits of several months duration.
PhD Project Description
PhD Project: Machine Learning methods for modeling and predictive control of dynamic systems:
Machine Learning (ML) techniques, and in particular Recurrent Neural Networks (RNN), are gaining a wide popularity in the control community in view of their ability to obtain, from plant data, models of complex dynamic systems, characterized by a strong nonlinear behavior. However, theoretical results related to control design methods, and in particular Model Predictive Control (MPC) based on RNN, are still needed and fundamental issuesmust be considered, with particular reference to (i) analyzing the properties of RNN in terms of Input/Output stability, (ii) observing the state of the network in real time, (iii) designing efficient MPC algorithms for embedded applications and with stability, tracking, and economic guarantees, (iv) defining optimization algorithms tailored to the adopted RNN structure for real-time control, (v) developing reconfiguration strategies integrated with monitoring procedures for fault tolerant MPC providing adequate levels of safety, (vi) dealing with multiple timescale systems. All these aspects will be dealt with in this PhD project, where different RNN structures will be considered, such as Echo State Networks (ESN), Gated Recurrent Unit (GRU) networks, and Long Short Term Memory (LSTM) networks. In addition, these structures will be used for the adaptive estimation and prediction of future disturbances acting on the system to allow for a tighter control action and for the design of scenario-based stochastic versions of MPC. In addition, other ML methods, like Reinforcement Learning methods, will be exploited to design efficient data driven control synthesis algorithms for industrial systems. The developed methods will be validated in real test benchmarks related to industrial control problems.
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, 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
Supervising team at the Politecnico di Milano:
Prof. Riccardo Scattolini;
Prof. Marcello Farina
Main Contacts at the ELO-X Partner Groups which could host secondments:
University of Freiburg: Prof. Moritz Diehl;
ODYS Srl: Prof. Alberto Bemporad;
Tool Temp: Dr. Jonas Asprion.
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 6”.
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