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archivedjobopenings:phds_working_on_process_mining_in_collaboration_with_dsc_e_and_philips [2016/12/06 11:38] (current)
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 +====== PhDs working on Process Mining in Collaboration with DSC/e and Philips ======
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 +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.
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 +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.
 +===== PhD positions on process mining ​ =====
 +The following four PhD positions will be related to the topic of process mining:
 +  - Product-centric Consumer Data Analytics: Product Usage Lifecycle Analysis [part of the >>​Data Driven Value Proposition<<​ theme] Digital components are being added to Philips lifestyle products. The data from these products as well as from Philips touch points must be combined to optimize user experience and maintain customer satisfaction. Process mining techniques will be used to analyze the usage of products over a longer period of time.
 +  - Transforming Event Data into Predictive Models [part of the **Healthcare Smart Maintenance** theme] 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.
 +  - Predictive Analytics for Healthcare Workflows
 +  - Radiology Workflow Optimization and Orchestration [both part of the **Optimizing Healthcare Workflows** theme] The delivery of patient care in hospital is a complex workflow based on fixed protocols. ​
 +Optimization of patient care at reduced cost requires the orchestration of multiple clinical workflows. ​ Timely getting the imaging/lab tests done and getting the results back to physicians can help quickly diagnose/​treat the patient, and save lives. The rapid digitization of diagnostics in radiology and pathology calls for a data-driven optimization of the workflows. Process mining will be used to learn models for the as-is situation. ​
 +However, process technology will also be used to improve the processes. Hence, people With a background in operations research, simulation and/or BPM are encouraged to apply.
 +===== How to apply =====
 +The Data Science Center Eindhoven (DSC/e) is looking for in total 18 PhDs in the area of data science. The entire project is divided into 5 themes. Within three themes there is a need for process mining (see above). If you are interested in a PhD on process mining please clearly indicate so in your application!
 +Apply via http://​jobs.tue.nl/​nl/​job/​18-phd-positions-on-data-science-joint-project-data-science-center-philips-190316.html. ​
 +===== Job qualifications =====
 +Candidates should:
 +  * have an MSc in Computer Science or a related discipline (e.g., Statistics or Operations Research)
 +  * have a strong interest in data science research and in process mining in particular
 +  * be highly motivated
 +  * able to develop software to allow for experimentation
 +  * be a fast learner, autonomous and creative, show dedication and be hard working
 +  * possess good communication capabilities and be an efficient team worker
 +  * be fluent ​ in English, both spoken and written
 +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 EIT doctoral training on entrepreneurship and related topics
 +  * be willing to work at two locations (TU/e campus and Philips High Tech Campus)
 +==== Appointment and salary ====
 +We offer:
 +  * a full-time temporary appointment for a period of 4 years, with an intermediate evaluation after 9 months
 +  * a gross salary of € 2,083 per month in the first year increasing up to € 2,664 per month in the fourth year
 +  * a holiday allowance of 8% and an end-of-year bonus of 8.3% (annually)
 +  * assistance in finding accommodation (for foreign employees)
 +  * the opportunity to perform research in a large-scale joint project from a leading technical university and a leading high-tech company
 +  * support for your personal development and career planning including participation in the EIT doctoral training, courses, summer schools, conference visits, research visits to other institutes (both academic and industrial),​ etc.
 +  * a broad package of fringe benefits (including excellent technical infrastructure,​ child day care, savings schemes and excellent sport facilities).
 +==== Selection procedure ====
 +Candidates must apply using the web form on this page, providing a detailed CV and a motivation letter that indicates one or more of the PhD projects that the candidate wishes to apply for.  Selection will be on-going in the period July 1, 2014 - November 1, 2014. This means that suitable candidates will be interviewed and if deemed fit, be hired immediately without waiting for applications of other candidates.
 +==== More information ====
 +For more information about the project, please contact [[http://​wwwis.win.tue.nl/​~wvdaalst/​|Wil van der Aalst]]. ​
 +Before asking very general questions, please make sure to have a good overview of the process mining work done at AIS.
 +For example, visit www.processmining.org,​ read http://​wwwis.win.tue.nl/​~wvdaalst/​cover_process_mining_book.pdf,​
 +and play with the ProM software. Also visit http://​wwwis.win.tue.nl/​~wvdaalst/​ and http://​www.win.tue.nl/​ais/​.
 +There is no detailed description of each PhD position. The process mining techniques to be applied and developed are/should be generic and will be used in different projects within Philips.
 +For information about employment conditions please contact Mrs. C.M. van Dam, HR advisor TU/e, email: pzwin@tue.nl,​ telephone +31 40 247 2735, or Mr. C.M. Kuiters, HR advisor TU/e, email: pzwin@tue.nl,​ telephone +31 40 247 2321.