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PhD Position in data-driven process control in Zürich

Studierende finden an der ETH Zürich ein Umfeld, das eigenständiges Denken fördert, Forschende ein Klima, das zu Spitzenleistungen inspiriert.

Jobbeschreibung

100%, Zurich, temporary

We invite applications for a PhD position that is part of a larger research project with the goal of demonstrating data-driven process control methods in a industrial manufacturing plant.

The successful candidates will join the Automatic Control Laboratory at the Department of Information Technology and Electrical Engineering as well as Inspire, the technology transfer unit at ETH Zurich under the supervision of Prof. John Lygeros and Dr. Alisa Rupenyan.

Project background

Industrial processes can be represented as cascaded structure where the outputs of one sub-stage in the process chain are the inputs of the next. Process control is built on the availability of these inputs and outputs, and on understanding the relationships between them, then implementing a control strategy to achieve process stability or certain quality characteristics. A process model based on the physics of the system is sometimes available, but its direct use could be limited to a certain class of problems, as it often involves complex, nonlinear physical dynamics models.

We propose to implement data-driven process modeling for the different sub-stages of an industrial process by establishing the input-output dynamic relationships at the substages of the process for use cases, where geometry, material, and type of process are specified. For each sub-stage, the input-output relationship can be learnt using data-driven techniques such as deep neural networks or gaussian process regression. In some cases, standard data-fitting techniques might be sufficient, or a simplified first principles model can be complemented by learning the model mismatch from data. Once the relationship between inputs and outputs is available at each stage, optimization-based techniques as model predictive control in combination with iterative learning, or extremum-seeking control can be applied.

Job description

This project offers the opportunity to be driving both method development, exploring various ways to integrate learning of the process parameters with optimal control methods, and practical implementation of the devised methods in an industrial environment.

The associated interdisciplinary PhD position available from October 2019, comprises several of the following activities: 

  • Proposing and integrating measurement/estimation techniques for the process variables, using sensor fusion
  • Development of process models using first principles and data-driven techniques (neural networks, gaussian process regression, data fitting / regression-based methods)
  • Development of feedback control of the process to achieve low variation in the output quality: prototype and validation tests on machine

This type of project is a collaboration with an industrial partner, and will be completed with the continuous support of a team of industry R&D experts, engineers, and doctoral students with background in mechanical, electrical, or chemical engineering.

Your profile

We are looking for a candidate with a Master’s degree from a recognised university; strong analytical skills and background in systems and control, process engineering, or mechanical engineering. Experience with data modeling / machine learning, as well as C++/Labview programming could be of benefit.

ETH Zurich
ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.
Interested?

We look forward to receiving your online application with the following documents: CV, statement of objectives or research interests, three contacts for reference, transcripts of all degrees, and one publication / thesis. Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Further information about the Automatic Control Lab can be found on our website , and for Inspire on . For questions about the position, please contact Mrs. Baumann, Tel +41 44 633 85 09 or email baumasab@control.ee.ethz.ch (no applications).

Veröffentlicht am

29-02-2024

Extra Informationen

Status
Inaktiv
Standort
Zürich
Jobart
Werkstudentenstelle
Tätigkeitsbereich
Technik / Elektronik
Führerschein erforderlich?
Nein
Auto erforderlich?
Nein
Motivationsschreiben erforderlich?
Nein

Zürich | Technik Stellenangebote | Werkstudentenstelle