[AISWorld] PhD on Process Mining for Predictive Models in Healthcare Smart Maintenance at DSC/e & Philips, Eindhoven, The Netherlands
Aalst, W.M.P. van der
W.M.P.v.d.Aalst at tue.nl
Wed Dec 17 11:33:39 EST 2014
PhD on Process Mining for Predictive Models in Healthcare Smart Maintenance (V32.2146) at DSC/e & Philips
In the context of the ongoing collaboration between the Data Science Centre Eindhoven (DSC/e) and Philips, we are 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". http://jobs.tue.nl/nl/vacature/phd-on-process-mining-for-predictive-models-in-healthcare-smart-maintenance-206476.html
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.
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<http://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.
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, stochastics, and optimization (i.e., computer scientists with an interest in operation research or mathematicians interested in computer/data science are encouraged to apply);
· candidates from non-Dutch or non-English speaking countries should be prepared to prove their English language skills;
· good communicative skills in English, both in speaking and in writing;
· candidates are expected to realize research ideas in terms of prototype software, so software development skills are needed.
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
· A full time temporary appointment for a period of 4 years, with an intermediate evaluation after 9 months;
· A gross salary of € 2083 per month in the first year increasing up to € 2664 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).
Information and application
· For more information about this position contact prof.dr.ir. Wil van der Aalst, e-mail: w.m.p.v.d.aalst at tue.nl or by telephone: +31 40 247 4295.
· For more information about the employment conditions contact drs. Charl Kuiters (HR advisor), e-mail: pzwin at tue.nl or by telephone: +31 40 247 2321.
The application should consist of the following parts:
· Cover letter explaining your motivation and qualifications for the position (the letter should also show an understanding of process mining and the work done within AIS, see websites such as www.processmining.org and the book "Process Mining: Discovery, Conformance and Enhancement of Business Processes");
· Detailed Curriculum Vitae;
· List of courses taken at the Bachelor and Master level including marks;
· Names of at least three referees.
Please apply via http://jobs.tue.nl/nl/vacature/phd-on-process-mining-for-predictive-models-in-healthcare-smart-maintenance-206476.html. Use the "Apply now" button of Vacancy V32.2146. Applications via e-mail will not be accepted.
Currently there are several open positions related to process mining and data science.
· Postdoc: Process Analytics for the European Data Science Academy
· PhD on Process Mining for Predictive Models in Healthcare Smart Maintenance
· Postdoc: Software Analytics and Process Mining
· PhD: Discovering Behavioral Software Models from Software Event Data
· Assistant Professor in Data Science (0.8-1.0 fte)
Please consult the web pages to get more information and apply.
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