Ongeveer 4 uur geleden - Universiteit Maastricht (UM) - Maastricht
De PhD positie is beschikbaar bij de afdeling radiologie van het Nederlands Kanker Instituut (NKI-AvL) en de aanstelling vindt plaats bij de Universiteit Maast…
Factories of the Industrie 4.0 era (smart industries) are not yet making full use of the huge amounts of collected data to optimize their maintenance operations. The EU Prophesy project will take maintenance to ...
The postdoc will work as statistical researcher in the context of the Prophesy European industrial research project. The main task is to develop and implement novel data analytic methods using ideas form from statistics, data mining and machine learning for use in adaptive and self-configuring predictive maintenance algorithms. The postdoc position offers a unique opportunity to get actively involved in applied research with state-of-the-art industrial plants (Industrie 4.0/Smart Industry). This will involve working on statistical challenges related to challenges like e.g. combining information from multiple sensors and monitoring changes as well as validating these methods by implementing them at the plants of the industrial partners within the consortium.
The postdoc will be part of a dedicated multidisciplinary research team of TU/e that involves members of the Statistics and Data Mining groups within Mathematics and Computer Science, as well as the Maintenance Group within the Industrial Engineering and Innovation Sciences department. This team is actively involved in large scale international industrial projects (including Prophesy, Mantis, Productivity 4.0) as well as several smaller industrial maintenance projects. At the industrial side, there is a close collaboration with industrial partners of the Prophesy project, in particular the Philips plant in Drachten (a prime example of smart industry/ Industrie 4.0 in the Netherlands). The project is embedded in the Smart Manufacturing and Maintenance Research Programme of the Data Science Center Eindhoven, and also connected to the Smart Maintenance programme within the Data Science Flagship, a strategic collaboration of TU/e with Philips HealthTech.
The Statistics group at TU/e focusses on statistical models for dynamic data. Research has both an applied and a fundamental character, with healthcare and technical systems as the two main application domains. It is part of the Stochastics section (STO) within the TU/e subdepartment Mathematics . The STO Section currently has 6 full professors, 2 associate professors, 10 assistant professors, 5 postdocs and 19 PhD students.
The STO section is actively involved in multidisciplinary research, in particular in the context of the Data Science Center Eindhoven. Moreover, EURANDOM a workshop and visitor centre in stochastics, is part of the section, and the staff of the section is heavily involved in its activities.
The Mathematics and Computer Science department consists of two subdepartments: Mathematics and Computer Science. Knowledge valorisation within the department takes place through Project Development Office (PDO). The department plays a key role in new Data Science Center Eindhoven, a research institute covering many facets of data science. In the education and research areas, the department works closely together with other universities and with companies. TU/e is located in Brainport, a high tech area around Eindhoven which includes the High Tech Campus, where 5000 industrial researchers from companies like Philips and NXP work together. This stimulating industrial environment enables TU/e to maintain close links with industry, healthcare and the building and logistics sectors.
Applicants are required to have a PhD degree in mathematics or statistics or have a comparable knowledge of statistics through a PhD degree in another field like data mining or machine learning. In view of the industrial context of the project, candidates should have interest in applied statistical research in an industrial context and be proficient with statistical software.
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