The Turku region of 320,000 inhabitants in Southwest Finland is one of Finland's leading centers of economic growth and technology development. Turku has a colorful history and high-quality education, services, and cultural offerings. Southwest of Turku spreads one of the most beautiful archipelagos in the world. The University of Turku is a world-class multidisciplinary research university that offers challenging tasks and a unique vantage point into the Finnish and international world of science and education. The University of Turku is the highest respected Finnish university outside Helsinki.

We are looking for a one research assistant for the newly established Department of Mechanical and Materials Engineering at the Faculty of Technology of the University of Turku (UTU). Research can be carried out alongside coursework, and it is suitable for master's thesis. The work is supervised by prof. Sampsa Laakso, prof. Wallace Moreira Bessa and postdoctoral researcher Kandice Suane Barros Ribeiro. The work is part of the Sustainable Materials and Manufacturing (SUSMAT) project of the University of Turku (UTU). After successful implementation of the project, the applicant may also have the opportunity to start postgraduate studies on the subject.

In this project, the student conducts machining experiments, the results of which are used as training data for the machine learning model being developed and as calibration and validation data for the element model. The finite element model is used to extend the machining experiments data domain, which is used for re-training the machine learning model. The aim is to extend the coverage of test data through hybrid modelling and eventually create an extended machine learning model capable of recommending optimal machining parameters and predicting the impact of the parameters on the machining process and the finished part.

The start date for this position is 01.07.2023 or as agreed and the duration of the contract is six months,  ending 31.12.2023.

Requirements

We welcome applicants from students with a background in mechanical or materials engineering, materials science, physics or computer science, or any other relevant field of engineering who are curious to learn machine learning, finite element modeling, and metal deformation behavior. Preference is given to students aiming to prepare their master's thesis, and interest in starting postgraduate studies is considered favorably. Previous experience in metalworking, experimental research, machine learning especially on the Python platform, and finite element modeling is considered an advantage.

Salary

Job-specific salaries are determined in accordance with the teaching and research staff of the universities' salary system. The salary of a research assistant is at level 1 of the standards chart. Depending on previous experience and competence, the salary at the beginning of the position is approximately 2200 € per month. The trial period is three (3) months.

Ready to apply?

Applications must be submitted no later than Thursday 8.6.2022 (at 23:59) using the university's electronic application form. The link to the system can be found at the beginning of the notification under "Fill in the application". The notice is available at: http://www.utu.fi/tyopaikat. Applications must be submitted in English and must be accompanied by the following documents:

(1) Motivation letter (max. 1 page, clearly state in the letter whether you want to do your master's thesis or not)

(2) Curriculum vitae

(3) Diplomas (if available) and transcripts of records

(4) Contact details or letters of recommendation of potential referees

The position will be filled as soon as a suitable person is found. For more information about the project, please contact prof. Sampsa Laakso (sampsa.laakso(at)utu.fi). If you have any questions about the application process, please contact HR Specialist Sarian Kotka (sjkkot(at)utu.fi). The University of Turku reserves the right, for justified reasons, to leave the position open, extend the application period, reopen the application process, and take into account applicants who have not submitted applications during the application period.