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PhD on Process Mining for Predictive Models in Healthcare Smart Maintenance

Function PhD-student
Departments Department of Mathematics & Computer Science
Institutes and others Data Science Center Eindhoven
Impuls-vacancies (promovendi) Yes
FTE 1,0
Date off 31/01/2015
Reference number V32.2146

Job description

In the context of the ongoing collaboration between the Data Science Centre Eindhoven (DSC/e) and Philips, we are still looking for a candidate with a strong background in process mining, data mining, stochastics, and/or predictive analytics for the PhD position “Transforming Event Data into Predictive Models”.

Context

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 (http://www.tue.nl/dsce/). The DSC/e has recently started a long-term strategic cooperation with Philips Research Eindhoven on three topics: data science, health and lighting. As a first concrete action, 70 PhD students are being hired for these three topics using joint funding from the TU/e and Philips, of which 18 PhD students will work on the data science topic. These students will together with researchers from the TU/e and Philips form a strong research community working together on scientific and industrial challenges. Most of the 18 PhD positions are filled now, but is still a vacancy for the PhD position “Transforming Event Data into Predictive Models which is part of the “Healthcare Smart Maintenance” theme.

Process Mining

The PhD will be appointed at the TU/e as a member of the AIS group, but also spend substantial time within Philips and co-location center at the high-tech campus in Eindhoven. The AIS group is one of the leading groups in the exciting new field of process mining (www.processmining.org). Process mining techniques focus on process discovery (extracting process models from event logs), conformance checking (comparing normative models with the reality recorded in event logs), and extension (extending models based on event logs). The work resulted in the development of the ProM framework that is widely used in industry and serves as a platform for new process mining techniques used by research groups all over the globe. Moreover, many of the techniques developed in the context of ProM have been embedded in commercial tools. See also www.processmining.org.

PhD position Transforming Event Data into Predictive Models

The position is part of the Healthcare Smart Maintenance theme of the ongoing collaboration between the Data Science Centre Eindhoven (DSC/e) and Philips. Philips has strong leadership positions in healthcare imaging and patient monitoring systems. In the healthcare domain, reducing equipment downtime and cost of ownership for hospitals is of vital importance. Smart maintenance exploits that professional equipment is connected to the internet and aims to use event and sensor data for overall cost reduction. Process mining techniques will be used to learn dynamic models that can be used for prediction and optimization.

Job requirements

Job requirements

We are looking for candidates that meet the following requirements:

Note that we are looking for candidates that really want to make a difference and like to work on things that have a high practical relevance while having the ambition to compete at an international scientific level (i.e., present at top conferences and in top journals).

Conditions of employment

Conditions of employment

We offer:

Information and application

More information:

The application should consist of the following parts:

You can apply by using the 'Apply Now' button on http://jobs.tue.nl/en/vacancy/phd-on-process-mining-for-predictive-models-in-healthcare-smart-maintenance-206476.html. Or follow the link: http://jobs.tue.nl/en/vacancies.html, choose Department of Mathematics and Computer Science and click ‘search’ to find this vacancy (V32.2146).