The Data Science Centre Eindhoven (DSC/e) is TU/e's response to the growing volume and importance of data and the need for data & process scientists (www.tue.nl/dsce/). Vanderlande is the global market leader in baggage handling systems for airports and sorting systems for parcel and postal services, and also a leading supplier of warehouse automation solutions (www.vanderlande.com/). Vanderlande recognizes the emerging trend of more data driven business models and addressed 'big data' a key topic on the technology roadmap. Therefore, under the umbrella of the 'Data Science Impuls' program, the DSC/e and Vanderlande joined forces in a research project with the aim of bringing holistic fact-based process analytics to logistics processes and automated material handling solutions. This by providing automated insight in process flows, process performance and bottlenecks to obtain improvement information in an efficient way (historical as well as real time).  

Process Mining in Logistics

The dynamics of logistics processes are notoriously difficult to analyze. Where classical process mining focuses on analyzing the processing of information associated to a specific unique case, logistics deals with physical objects that are grouped and processed together with other physical objects in one process at one or more physical locations, then distributed and later re-aggregated with other physical objects in another process at other physical locations. Consequently, processes reveal a multi-dimensional nature when looking at the performance of flows across networks of logistics.  In essence, logistics deals with numerous processes, cases, and objects that interact with each other in a multi-dimensional fashion making it impossible to pick a single appropriate viewpoint on the data for analysis and improvement.

The goal of the joint research project of DSC/e and Vanderlande is to lift process mining to this multi-dimensional space, and to allow analyzing logistics processes and systems from all relevant angles and viewpoints. By having thorough and fast insights into logistics and business processes, improvements can be found, predicted, and implemented at Vanderlande delivered logistics solutions.

Over the last year, we laid groundwork in novel techniques for data extraction, Big Data processing, and process discovery for multi-dimensional event data that will be continued in the next years. The upcoming research and this PhD project focuses on:

Predictions of process outcomes and online recommendations, enabled by

Scalable process discovery and deviation detection techniques for multi-dimensional event data.

To enable these, the project requires further development and improvement on:

appropriate data extraction from logistics systems and Big Data processing techniques, and

appropriate (conceptual) models of logistics processes and systems.

Positions

The project has two PhD positions within the joint research project of DSC/e and Vanderlande. The first PhD candidate started in September 2016, this vacancy solicits for the second PhD candidate. The two PhDs join the Analytics for Information Systems group (AIS) at Eindhoven University of Technology (TU/e) and will be supervised by Dirk Fahland, Boudewijn van Dongen (TU/e), and Wil van der Aalst (RWTH, TU/e). In addition, the two PhDs will closely collaborate and spend significant time within Vanderlande. In this context, the two PhDs will focus on applying, extending, and developing process mining to the requirements elicited together with Vanderlande. In particular, the results obtained in the project shall both be implemented in software prototypes for validation and research as well as disseminated in trainings. The two PhDs are expected to closely collaborate on the project and solve the upcoming challenges jointly from different angles.

Vanderlande provides its expertise, engineering capabilities, and data for deriving accurate and realistic requirements for various kinds of logistics solutions and problems as well as the opportunity to quickly validate all ideas in a realistic setting. The Analytics for Information Systems group (AIS) of TU/e provides its long running expertise and experience across all challenges of process mining in general and artifact-centric process mining specifically.

We are looking for candidates that meet the following requirements

a solid background in Computer Science, Data Science, or Mathematics (demonstrated by a relevant Master);

ideal candidates have a strong background in process/data mining, logistics, and/or databases (in particular data modeling and query construction);

have a strong interest in data science research;

can demonstrate familiarity with predictive analysis using data mining, process mining, and/or statistical methods,

have the ability to realize research ideas in terms of prototype software, so software development skills are needed.

are highly motivated, rigorous, and disciplined when developing algorithms and software according to high quality standards;

good communication skills in English, both in speaking and in writing (candidates from non-Dutch or non-English speaking countries should be prepared to prove their English language skills);

possess good communication capabilities and be an efficient team worker.

PhD students are expected to:

perform scientific research in the domain described

collaborate with other researchers in this project

present results at (international) conferences

publish results in scientific journals

participate in activities of the group and department, at both sites

assist in teaching undergraduate/graduate courses

participate in doctoral training on relevant topics

be willing to work at two locations (TU/e campus and at Vanderlande)

provide training to internal specialists at Vanderlande

We offer:

A full time temporary appointment for a period of 4 years, with an intermediate evaluation after 9 months;

A gross salary of € 2222 per month in the first year increasing up to € 2840 in the fourth year;

Support for your personal development and career planning including courses, summer schools, conference visits etc.;

A broad package of fringe benefits (e.g. excellent technical infrastructure, child daycare and excellent sports facilities).