Research Assistant/Accociate (F/M/D)- Cinema - Machine Learning In Model Predictive Control

DC4

Institution

Lehrstuhl für Systemverfahrenstechnik

Our Profile

The chair of Process Systems Engineering (AVT.SVT) is part of Aachener Verfahrenstechnik at RWTH Aachen University, was founded in 2012 and is directed by Prof. Alexander Mitsos, PhD. Our research covers different disciplines by combining methods of classical engineering, computational science and mathematics. The method oriented research is focused on the development of techniques for
mathematical modelling as well as process development and control. Besides that, importance is placed on the systematic integration of
experimentation and modelling plus the development of numerical algorithms and software tools. The method oriented emphasis is supplemented by industrial applications of our work.

The control of chemical reactors is a well-studied problem but is especially challenging in the case of polymerization reactors because many of the important characteristics of the polymer evolve continuously with time and cannot be measured directly online. Aided by first-principles mathematical models of the polymerization process, CINEMA aims to address this problem by using machine learning to accelerate the prediction of the evolution of the polymerization such that reactors can be controlled on-line. Polymeric systems often have elaborate structure-property relationships that are challenging to accurately establish using only first-principles models. In CINEMA this issue will be overcome by combining first principles and machine learning approaches such that they can be trained with comparatively small amounts of experimental data. This “science-informed” machine learning approach will be applied to industrially relevant products such as waterborne coatings and adhesives.

Your Profile

• MSCA-recruiting rules are applied: not having resided in Germany for more than 12 months in the 3 years immediately before the recruitment date, and not having carried out their main activity (work, studies, etc.) in Germany during this period.
• Having a master degree or equivalent diploma, and not having a doctoral degree. Master’s degree in chemical/process engineering, polymer engineering, computational engineering, mathematics, computer science, energy systems engineering, or a related subject.

Excellent command of written and spoken English is a must. Ability for research management, dissemination, communication
with colleagues and supervisors, strong teamwork spirit, creativity and problem solving. Solid programming skills, knowledge of
numerical optimization. Research experience in the academic or industrial sector will be considered

Your Duties and Responsibilities

The fellowships are funded as part of the Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks
under the European Commission’s Horizon 2021 programme. Your tasks within our CINEMA project
are: Machine learning applied to accelerate the computation of the optimization problem.Controller model simplification using
machine learning. Combining ML-driven fast update model predictive control methods for hybrid models.

You can find all important information and the application portal here:
https://cinema-dn.eu
Please apply directly here: https://cinema-dn.eu/open-positions/ DOCTORAL CANDIDATE 4

What We Offer

The successful candidate will be employed under a regular employment contract.
The position is to be filled by 7/1/2023 and offered for a fixed term of three years.
The fixed-term employment is possible as it constitutes one of the fixed-term options of the Wissenschaftszeitvertragsgesetz (German Act on Fixed-term Scientific Contracts).
This is a full-time position.
The successful candidate has the opportunity to pursue a doctoral degree in this position.
The salary is based on the German public service salary scale (TV-L).
The position corresponds to a pay grade of EG 13 TV-L.

About us

RWTH is a certified family-friendly University. We support our employees in maintaining a good work-life balance with a wide range of health, advising, and prevention services, for example university sports. Employees who are covered by collective bargaining agreements and civil servants have access to an extensive range of further training courses and the opportunity to purchase a job ticket.
RWTH is an equal opportunities employer. We therefore welcome and encourage applications from all suitably qualified candidates, particularly from groups that are underrepresented at the University. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of national or ethnic origin, sex, sexual orientation, gender identity, religion, disability or age. RWTH is strongly committed to encouraging women in their careers. Female applicants are given preference if they are equally suitable, competent, and professionally qualified, unless a fellow candidate is favored for a specific reason.
As RWTH is committed to equality of opportunity, we ask you not to include a photo in your application.
You can find information on the personal data we collect from applicants in accordance with Articles 13 and 14 of the European Union's General Data Protection Regulation (GDPR) at http://www.rwth-aachen.de/dsgvo-information-bewerbung.