Doctoral in Automation , Tampere University of Technology, Professor Risto Ritala: Doctoral student in Automation (Just-in-time wood raw material procurement based on harvester Big Data)
The Doctoral school of Industrial Innovations, DSII, is a novel concept of Tampere University of Technology. The key idea of the DSII is that the research questions are industry originated and partially financed by a company. Tampere University of Technology employs doctoral student to carry out the dissertation research. The contract will be made for 4 years and the dissertation should be completed in that time. Research work is supervised by a professor and company representative. The work will be done at Tampere University of Technology and partially at the company.
The Doctoral school of Industrial Innovations offers active community for doctoral students. DSII has monthly follow up meetings, trainings and other activities that support research work and activities are organized with Hermia Group and DEMOLA.
Job description:
The tasks of the Doctoral Student will comprise designing a sensor system and related data analysis tools for a forest harvest to assess the traversability and driving resistance of forest soil. Furthermore the Student will develop decision support systems that combine this data with forest inventory data, weather data etc in order to enhance both the availability of the harvester and efficiency of the harvesting operations.
The work will be supervised by Professor Risto Ritala and Jarmo Hämäläinen (Metsäteho Oy). The position will be located at the Department of Automation Science and Engineering.
The following five research questions from the core of this doctoral study project:
- What kind of automatic sensor system (the simpler the better for actual productisation) could reliably enough measure/estimate the key parameters of forest ground and harvesting quality?
E.g. Soil composition, humidity, stoniness, bearing capacity
• Ground rutting depth and other harvesting quality parameters - Can machine operation and performance measurements, such as drive transmission pressure data describing the driving resistance, be used to estimate ground properties with adequate accuracy?
- What kind of added value could be gained by using available multi-dimensional multisource information available in forestry domain?
Fixed position based information such as soil type, LiDar elevation model etc.
• Forest parameters such as stem number, possible future tree map etc.
• Time critical parameters such as local rainfall etc. - Can harvester machine serve the needs of other information users as a mobile weather station based on CANbus connected sensor data of air temperature, air pressure, GPS location etc.?
Requirements:
The Doctoral Student candidate must hold an applicable higher university degree in for example sensors & measurement technology, automation, or data analysis. Knowledge on forestry machines, automation buses in mobile machines, GPS, LIDAR, or estimation/data fusion is a benefit.
The Doctoral Student candidate must have an enquiring mind, enjoy problem-solving and be able to think independently while also being able to work in a team. A good command of English, both in writing and in oral presentations, is mandatory.
How to apply:
Applications must be submitted by TUT online application form. The closing date for applications is 12 October 2015 (23:59 EEST (GMT+3)). Applications and all accompanying documentation must be in English.
The application should include a letter of motivation (max. one A4 page), CV, publication list and transcript of record (master’s degree studies).
Last date: 12 October 2015
For more information: risto.ritala@tut.fi.