Master's Thesis: Design of Experiments (DoE) & Modeling of Active Thermography

Fraunhofer-Gesellschaft · Augsburg

ThesisGerman needed: not said

Still open on 2 Nov

You apply on the company's own website.

Key facts

Pay: Paid by Fraunhofer's fixed rules for student helpers
Job starts: As soon as possible
Subject you study: Electrical Engineering, Mechanical Engineering, Physics, Production Engineering, Data Science

Parameter identification: Identifying relevant process and measurement parameters (e.g. excitation power, pulse duration) as well as disturbance factors (e.g. ambient conditions, material variability) and quantifying their influences
DoE design: Developing and carrying out a statistical experimental plan (Design of Experiments) for active thermography to test contaminated battery electrodes
Modeling: Creating a regression model to predict the detectability of contamination depending on the identified parameters; deriving optimal test parameters

Master's studies in electrical engineering, mechanical engineering, physics, production engineering, data science or similar
Solid knowledge of statistical design of experiments (e.g. response surface methodology)
Experience with regression models and data analysis, ideally in Python
Independent, structured and analytical way of working; initiative when running experiments on the test bench

We offer flexible working hours, excellent equipment and the chance to actively help shape sustainable mobility solutions with us.
As part of a friendly, motivated team, you take part in exciting research projects and take on responsible tasks with a lot of creative freedom.

Parameter identification: Identifying relevant process and measurement parameters (e.g. excitation power, pulse duration) as well as disturbance factors (e.g. ambient conditions, material variability) and quantifying their influences
DoE design: Developing and carrying out a statistical experimental plan (Design of Experiments) for active thermography to test contaminated battery electrodes
Modeling: Creating a regression model to predict the detectability of contamination depending on the identified parameters; deriving optimal test parameters

Master's studies in electrical engineering, mechanical engineering, physics, production engineering, data science or similar
Solid knowledge of statistical design of experiments (e.g. response surface methodology)
Experience with regression models and data analysis, ideally in Python
Independent, structured and analytical way of working; initiative when running experiments on the test bench

We offer flexible working hours, excellent equipment and the chance to actively help shape sustainable mobility solutions with us.
As part of a friendly, motivated team, you take part in exciting research projects and take on responsible tasks with a lot of creative freedom.