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 positions at the University of Freiburg have a strong methodological focus in the field of computational control and mathematical optimization. They are based in the Systems Control and Optimization Laboratory (Syscop) at University Freiburg headed by Prof. Moritz Diehl. Their aim is the development of advanced optimal control methods and open-source software and their application to industrially relevant optimization and estimation problems. While these methods are generic and applicable in several branches of engineering, they shall be tested and used in close cooperation with the the other ELO-X PhD fellows, in particularly with those who are based in TOOL-TEMP, Mitsubishi Electric Research Laboratories (MERL) and Stanford University during mutual exchange visits of several months duration.
PhD Project Description
PhD Project: Embedded nonlinear MPC solvers with sparsity exploitation for learned model.
The combination of machine learning and model predictive control can potentially allow for efficient control of complex systems with very little engineering effort, as both the task of modeling the system and finding the best way to control it is handled by the computer. However, learned models are usually larger and hence require more computational effort to simulate and optimize than hand-crafted models. The objective of this project is to improve the speed of model predictive control (MPC) algorithms for learned models, by developing nonlinear optimization algorithms that exploit the structure and sparsity patterns that arise in these specific optimization problems, and by developing “optimization friendly” model classes. Such models must be efficiently evaluated and differentiated inside the embedded optimization solvers.
Timeline and remuneration:
The ideal starting time is in early summer 2021. The PhD projects last for the duration of three years, and are carried out at the Systems Control and Optimization Laboratory headed by Prof. Dr. Moritz Diehl at the University of Freiburg, Germany. 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 identification of optimization friendly learning models to be used with MPC. The second year focuses on optimized learned models that can be evaluated fast and have small memory footprint and have the potential for sparsity exploitation. In the third year, the plan is to focus on open-source software implementation, algorithm verification and integration into rapid prototyping frameworkds for embedded control systems. A fourth PhD year can be added and funds are reserved for this at the University of Freiburg. 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 University of Freiburg:
Prof. Dr. Moritz Diehl (head of systems control and optimization laboratory);
Dr. Gianluca Frison (Postdoc at Syscop working on high-performance linear algebra and QP solvers for embedded optimization);
Katrin Baumgärtner (PhD student focusing on efficient algorithms for MHE and Iterative Learning Control);
Florian Messerer (PhD student working on robust optimal control and sequential convex programming);
Jonathan Frey (PhD student working on efficient software for MPC and MHE, acados).
Main Contacts at the ELO-X Partner Groups which could host secondments:
Dr. Jonas Asprion (TOOL-TEMP);
Dr. Rien Quirynen (Mitsubishi Electric Research Laboratories);
Prof. Stephen Boyd (Stanford University).
Ideal candidates have a master degree in one of the following disciplines or a related field: control engineering, numerical mathematics, computational physics or computer science. They should have a good background or interest in mathematical optimization, dynamic system modelling and simulation, and programming (C/C++, Python, Matlab), 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.
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 2”.
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 Germany 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