We are seeking a highly creative and motivated Professional Doctorate candidate to tackle new challenges in predictive maintenance in collaboration with the TU/e Data Mining group and Strukton Worksphere. Strukton Worksphere is a technical service provider who is responsible for maintaining and operating many large offices, hospitals, datacenters and other types of buildings in the Netherlands. For proper and efficient operations, Strukton collects and uses a lot of data of buildings, energy consumption, equipments data, realtime measurements and data of the provided maintenance services. For Strukton Worksphere, 'predictive maintenance' is one of the focus points and we believe that based on our data, intelligent model predictions can be used to predict failures or maintenance actions in the near future.

Your challenge is to:

Design models for failure prediction on HVAC (Heating, Ventilation and Air-conditioning) equipment for predictive maintenance;

Define a general methodology for predictive maintenance in the workfield of Strukton Worksphere. The workfield of Strukton Worksphere is characterised by a high variety of techniques, OEMers (manufacturers), surroundings, and low frequency SCADA data (Supervisory control and data acquisition);

Contribute to design and implementation of a scalable architecture for the methodology;

Collaborate with a team of specialists from Strukton Worksphere to bring the methodology into practice.

To develop a general methodology, several cases on equipments/techniques will be identified in which this methodology will be determined. The cases will be chosen in consultation with Strukton Worksphere based on business value, cooperation with OEMers and data availability. The first cases which are already in scope are the following:


Case 1: Predictive maintenance of Elevators

- Predicting short term errors and required maintenance

- Based on measurement data and failure and maintenance history

- Maximize uptime


Case 2: Predictive maintenance of Chillers

- Explore the possibilities of predictive maintenance, specifically for cooling equipment in high risk environments like datacenters

- Predicting short term errors and required maintenance

- Maximize uptime

This work is set in an very interactive environment, including the Eindhoven Data Mining Group and Strukton Workphere. The availability of extensive data and expertise offers a unique opportunity for a bright student to tackle this challenge.

We are looking for an excellent and highly motivated candidate with:

MSc degree in Data Science or a related subject;

Knowledge / interest of Building Physics and Services;

An excellent command of the English language, both in speaking and writing;

An interest in developing new sustainable technologies and applications for the built environment;

An academic attitude in order to develop new technologies and perform applied research with an integral view on what is needed for the future built environment;

Strong communication skills. The ability to collaborate with members of a team is of paramount importance.

We offer:

a challenging job in a dynamic and ambitious university and a stimulating research environment;

a full time temporary appointment for a period of 2 years with a gross salary of €1.828 per month;

an extensive package of fringe benefits (e.g. support in moving expenses and commuting expenses, excellent technical infrastructure, on-campus child care, and excellent sports facilities, extra holiday allowance (8%, May), and end-of-year bonus (8.3%, December)).