Postdoc Statistical Analysis for Optimized Predictive Maintenance Services
Postdoc Statistical Analysis for Optimized Predictive Maintenance Services
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Job description
Factories are not yet making full use of big data to optimize maintenance operations. The EU Prophesy project aims to develop an integrated predictive maintenance platform. We are looking for a researcher that will help us develop algorithms that combine methods from statistics and machine learning in an exciting industrial context.
The postdoc will work as statistical researcher in the context of the Prophesy EU industrial research project. The main task is to develop and implement novel data analytic methods for predicting RUL (Remaining Useful Life) by combining ideas from statistics and artificial intelligence into a three-level approach involving detection, root cause analysis and prediction. This will involve working on statistical challenges related to changepoint detection, causal inference and degradation modelling as well as validating these methods by implementing them at the plants of the industrial partners within the consortium. The postdoc position offers a unique opportunity to get actively involved in applied research with state-of-the-art industrial plants (Industry 4.0/Smart Industry).
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/ Industry 4.0 in the Netherlands). The project is embedded in the Smart Manufacturing and Maintenance Research Programme of the Data Science Center Eindhoven.
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 but is likely to expand in the near future.
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.
The postdoc will work as statistical researcher in the context of the Prophesy EU industrial research project. The main task is to develop and implement novel data analytic methods for predicting RUL (Remaining Useful Life) by combining ideas from statistics and artificial intelligence into a three-level approach involving detection, root cause analysis and prediction. This will involve working on statistical challenges related to changepoint detection, causal inference and degradation modelling as well as validating these methods by implementing them at the plants of the industrial partners within the consortium. The postdoc position offers a unique opportunity to get actively involved in applied research with state-of-the-art industrial plants (Industry 4.0/Smart Industry).
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/ Industry 4.0 in the Netherlands). The project is embedded in the Smart Manufacturing and Maintenance Research Programme of the Data Science Center Eindhoven.
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 but is likely to expand in the near future.
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.
Specifications
- max. 38 hours per week
- Eindhoven View on Google Maps
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Eindhoven University of Technology (TU/e)
Requirements
Applicants are required to have a PhD degree in mathematics or statistics. Applicants with a PhD degree in data mining or machine learning that have a keen in statistics are also encouraged to apply. 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.
Conditions of employment
We offer:
- A full-time temporary appointment for 1 year;
- Salary in accordance with CAO of the Dutch universities (scale 10 € 2.709 - € 4.274 depending on experience;
- An extensive package of fringe benefits, including excellent technical infrastructure, child care, savings schemes, and excellent sports facilities, extra holiday allowance (8%, May), and end-of-year bonus (8.3%, December);
- Foreign applicants may benefit from the 30% tax regulation in order to get a higher net salary, when granted.
