Research Assistant / Associate (f/m/d)

Data Science & Machine Learning with a focus on Process Mining



Natasa Marcanova


work Phone
+49 4218021902



Informatik 9 - Process and Data Science

Our Profile

As part of the Network for National High Performance Computing (NHR) , RWTH Aachen University and TU Darmstadt combine their strengths in High Performance Computing (HPC) applications, algorithms and methods as well as in the efficient allocation and use of HPC hardware. As a joint NHR Center for Computational Engineering Sciences (NHR4CES), the two sites provide targeted support for engineering applications, especially with regard to complex flow scenarios, energy conversion, materials design, and engineering-oriented physics, chemistry, and life sciences. In this context, Data Science and Machine Learning play a central role. In total, there will be four positions, with one focusing on Process Mining (including process discovery, conformance checking, performance analysis, predictive analytics, operational support, and process improvement).

Your Profile

We are looking for candidates that:

ohave a university degree (master or equivalent) in a strong technical field (Computer Science, Software Engineering, Data Science) or a comparable education,
ohave advanced experience in software engineering and enjoy creating sophisticated software systems,
ohave a genuine interest (or experience) in data science and machine learning, in particular Process Mining, and are willing to demonstrate this as part of the application process
ohave proven to belong to the top of your graduating class as evidenced by your marks and supported by your references,
ohave excellent analytical skills, and you are eager to implement your ideas in software and make them genuinely scalable,
ohave excellent English verbal and written communication skills,
oare service-oriented and able to communicate with various stakeholders in a professional manner.

Your Duties and Responsibilities

The Cross-Sectional Group (CSG) Data Science and Machine Learning is a working group and support structure in NHR4CES and specifically supports the application areas defined there, by contributing methodological expertise to software development, selecting suitable tools, and thus advancing simulation within NHR4CES. The CSG Data Science & Machine Learning is led by professors Wil van der Aalst (RWTH), Kristian Kersting (TU Darmstadt), and Bastian Leibe (RWTH). Of the four positions, this position will focus on the interplay between high-performance computing, machine learning, and process science. The work for this position will be mostly conducted within the Process and Data Science (PADS) group at RWTH Aachen University (led by prof. Van der Aalst) while being embedded in the larger NHR4CES ecosystem.

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, AI, 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.

In the NHR4CES, the goal is to make Process Mining better scalable and better applicable. This involves using the HPC infrastructure, developing novel algorithms and tools, and applying these to problems in various domains (health, production, energy, mobility, etc.).

Your tasks will include:

You will design, develop, and maintain software for cutting-edge research in data science, machine learning, and process mining.
You will contribute to open-source data-science platforms like ProM, PM4Py, RapidMiner, R, etc.
You will work with other partners that want to analyze process-related data.
You will guide Bachelor and Master students developing software and thus have a limited involvement in teaching.

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 for 1 . An extension of at least 2 years is provided, of 3 years is possible.
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 corresponds to pay grade EG 13 TV-L of the German public service salary scale (TV-L).
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. We also offer a comprehensive continuing education scheme and a public transportation ticket available at a significantly reduced price.
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

Number : 33923
Application deadline : 30.09.2021
Mailing Address : RWTH Aachen University
Informatik 9 - Process and Data Science
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.