Research Assistant/Associate (f/m/d) - Postdoc

Contact

Name

Mara Nitschke

Telephone

workPhone
+49 241 80-21902

E-Mail

Institution

Lehrstuhl für Process and Data Science

Our Profile

The Process and Data Science (PADS) group, headed by Prof. Dr. Ir. Wil van der Aalst, is a research group at RWTH focusing on the interplay between processes and data. PADS symbolizes RWTH-s ambitions in the area of Data Science and is supported through Alexander von Humboldt Professorship. The scope of PADS includes all topics where discrete processes are analyzed, reengineered, and/or supported in a data-driven manner. Process-centricity is combined with an array of Data Science techniques (machine learning, data mining, visualization, and Big data infrastructures). The main focus is on Process Mining (including process discovery, conformance checking, performance analysis, predictive analytics, operational support, and process improvement). This is combined with neighboring disciplines such as operations research, algorithms, discrete event simulation, business process management, and workflow automation.

The chair of PADS is the founder of the process mining discipline and one of the leading computer scientists in
the world. The ambition is to realize scientific breakthroughs which will help organizations to turn event data into business and societal value. Investments by RWTH, the Alexander von Humboldt foundation, and the Fraunhofer Institute for Applied Information Technology make it possible to realize these ambitions and to provide unique opportunities for ambitious Postdocs.

Your Profile

  • You have a doctorate/PhD in computer science or a related discipline (e.g., statistics, operations research or management science with a specialization in data and/or process science).
  • You have presented your work at international conferences (BPM, ICPM, CAiSE, ER, etc.) and/or published in relevant journals.
  • You are a fast learner, dedicated, autonomous and creative.
  • You have experience in supervising Bachelor and Master students.
  • You have a proven track record in one or more of the following disciplines: data science, process science, process mining, machine learning, process management, and/or operations research.
  • You have worked on process mining or closely related topics (e.g., read the process mining book https://www.springer.com/de/book/9783662498507 or took the Coursera MOOC "Process Mining: Data science in Action" https://www.coursera.org/learn/process-miningYou have excellent analytical skills and you are willing to implement your ideas in software together with PhDs and Master students.
  • You are ambitious, but at the same time a team player.
  • You are eager and able to (co-)supervise PhD students and take a leading role in research projects.
  • You have excellent language skills (English) and willing to learn German.

Your Duties and Responsibilities

  • You will do cutting-edge process mining research in one of the top groups in data science.
  • You will take a leading role in research projects with industrial partners that provide data and give feedback on research results.
  • You will supervise PhD, Bachelor and Master students working on related topics and have a limited involvement in teaching.
  • You will present your work at national and international conferences and publish in the leading journals in your discipline.

What We Offer

The successful candidate will be employed under a regular employment contract.
The position is to be filled at the earliest possible date and offered for a fixed term of 1 year.
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 with the possibility of a part-time contract upon request.
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.

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.

Application
Number:V000002132
Application deadline:30/09/2022
Mailing Address:RWTH Aachen University
Lehrstuhl für Process and Data Science
Mara Nitschke
Ahornstraße 55
52074 Aachen
Email:
Applicants are invited to submit their applications via email. For data protection reasons, however, we recommend sending applications via mail.