[AISWorld] Research Assistant / Ph.D. Position in Process Mining at the Fraunhofer Institute for Applied Information Technology (FIT) in Aachen

[van der Aalst, Wil]

>>  Research Assistant / Ph.D. Position in Process Mining at the Fraunhofer Institute for Applied Information Technology (FIT) in Aachen <<
https://recruiting.fraunhofer.de/Vacancies/35563/Description/1

A new research group working on the interplay between data science and process science has been established within the Fraunhofer Institute for Applied Information Technology (FIT). We are looking for a Research Assistant / Ph.D. candidate to strengthen the team. The focus will be on automated process improvement based on event data. We encourage people interested in the interplay of processes and data to apply. This is a unique possibility to do a Ph.D. while being exposed to real-world event data and challenging problems that are relevant from both scientific and practical point of view.

Fraunhofer FIT closely collaborates with the Process and Data Science (PADS) group, headed by Prof. Wil van der Aalst, at RWTH Aachen University. The new FIT research group will be collocated with the PADS group in Aachen, Germany. The scope of the new group includes all topics where discrete processes are analyzed, reengineered, and/or supported in a data-driven manner (www.pads.rwth-aachen.de). 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 ambition is to realize scientific breakthroughs that will help organizations to turn event data into business and societal value.

Recent breakthroughs in process mining research make it possible to discover and analyze operational processes based on event data. More and more events are recorded by a wide variety of systems (cf. internet of things, social media, mobile devices, web services, etc.). The spectacular growth of event data provides many opportunities for automated process discovery based on facts. Moreover, event logs can be replayed on process models to check conformance and analyze bottlenecks. The uptake of process mining is reflected by the growing number of commercial process mining tools available today. There are over 25 commercial products supporting process mining (Celonis, Disco, Minit, myInvenio, ProcessGold, QPR, etc.). All support process discovery and can be used to improve compliance and performance problems. For example, without any modeling, it is possible to learn process models clearly showing the main bottlenecks and deviating behaviors. However, still missing are reliable techniques to improve operational processes automatically. The Fraunhofer Institute for Applied Information Technology (FIT) research group working on process mining will focus on new technologies making it possible to support automated process improvement in a data-driven manner.

Existing process mining techniques can be used to diagnose problems, but the transition from "as-is" to "to-be" models is not supported. Traditional approaches aiming at process improvement are often not data-driven (i.e., event data are only used indirectly), require expert knowledge (e.g., to build a simulation or queueing model), and provide only answers to "what-if" questions. Without groundbreaking innovations, it is impossible to provide generic tools that automatically suggest process improvements based on event data. The Fraunhofer AOPI (Automated Operational Process Improvement Using Process Mining) project aims to address these challenges by realizing the breakthroughs needed to create a new breed of process mining tools. In AOPI we will develop the technology making it possible to support automated process improvement in a data-driven manner. Through guided process discovery (exploiting domain knowledge and observed behavior), we aim to learn faithful "as-is" models. Through comparative process mining, we want to identify the essential factors influencing performance. Through process constraint elicitation, we set the boundaries for process improvement. The results will be used for unprecedented forms of operational process support and automated process improvement.
The challenges addressed in AOPI are of huge practical relevance. Existing process mining tools do not provide these capabilities. Both tool vendors and users of these tools see the need to support automated data-driven process improvement. The unique capabilities offered by process mining, the availability of torrents of event data, and the support of FIT and RWTH offer a unique opportunity to realize breakthroughs needed. The goal of the Ph.D. project is to develop a comprehensive approach supported by novel process mining techniques. Proof-of-concepts will be realized based on existing open-source tools like ProM. Moreover, ideas will be transferred to commercial products in close collaboration with industry.

Your profile
*	You are eager to become a data science researcher and have a Master 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 proven to belong to the top of your graduating class as evidenced by your marks and supported by your references.
*	You are a fast learner, dedicated, autonomous and creative.
*	You have a genuine interest (or experience) in process mining and are willing to demonstrate this as part of the application process.
*	You have excellent analytical skills and you are willing to implement your ideas in software.
*	You are ambitious, but at the same time a team player. 
*	You have excellent communicative skills (also in English).

More information and How to apply?

We welcome applications from people that combine strong technical skills with the desire to solve problems that matter in the real world. For more information about the conditions and how to apply: please visit https://recruiting.fraunhofer.de/Vacancies/35563/Description/1. Also visit www.pads.rwth-aachen.de, www.vdaalst.com, and http://www.fit.fraunhofer.de for background information. Use the reference FIT-2018-1 when applying. For questions, please use the e-mail address applications at pads.rwth-aachen.de. 

We are looking forward to your application!

____________________________________
  Prof.dr.ir. Wil van der Aalst
  Process and Data Science @ RWTH
  www.vdaalst.com