Modern industrial systems generate ever-increasing amounts of data across the entire value chain, such as event logs, sensor measurements, service logs, customer reviews, etc. Mining this data offers the opportunity to gain valuable insights about individual stages of the value chain as well as complex relationships between the different stages. For example, an interesting challenge is to explore how data mining techniques can be used for finding links between the end-user behavior and a manufacturing process, in order to improve process quality control for failing products and to identify and validate new features.

The research performed by the candidates will address practical challenges of mining and processing industrial data, such as (i) distributed processing techniques which can support data mining in settings with bandwidth and/or real-time constraints, e.g. when the prediction of device failures is distributed between devices and a cloud, (ii) privacy aware mining of customer behavior data, e.g. when monitoring the daily usage patterns of a smart electric shaver, (iii) coping with missing data, e.g. due to misconfigured or broken sensors, (iv) methods for exploring data mining across the value chain in case raw data is not available.

The results will be demonstrated within the European project Productive 4.0, where the goal of TU/e is to develop techniques for analyzing the data pertaining to the production and usage of consumer electronics products (e.g. electric shavers) by one of our industrial partners (Philips). Depending on their interests and their focus the candidates will be assigned to one two groups: the Data Mining group or the System Architecture and Networking group.

We are looking for candidates that meet the following requirements:

PhD in Computer Science

Experience with data mining or machine learning

Ability to combine different research techniques and apply them to large scale industrial systems.

Good communicative skills in English, both in speaking and in writing. Candidates should be prepared to prove their English language skills.

The Postdoc is expected to:

perform scientific research in the domain described;

collaborate with other researchers in this project;

publish results in leading scientific journals and conferences in the field;

assist in guiding (project-related) MSc graduation projects.

full-time employment as a Postdoc for a period of initially 1 year, after a positive evaluation the employment can be extended with 2 years;

annually 8% holiday allowance and 8.3% end of year allowance;

support with your personal development and career planning including courses, conference visits etc.;

a broad package of fringe benefits (including an excellent technical infrastructure, child care, moving expenses, savings schemes and excellent sports facilities).