Master Thesis: Evaluation of Machine Learning - Munich, Deutschland - Fraunhofer-Gesellschaft

Fraunhofer-Gesellschaft

Geprüftes Unternehmen

Munich, Deutschland

vor 3 Wochen

Geschrieben von:

Lena Wagner

beBee Recruiter

Beschreibung

Fraunhofer EMFT conducts cutting-edge applied research on sensors and actuators for people and the environment.

The about 150 employees in the three locations in Munich, Oberpfaffenhofen and Regensburg possess impressive long-term experience and wide-ranging know-how in the fields of microelectronics and microsystem technology.

The technology offering of the research institute ranges from semiconductor processes, MEMS technologies and 3D integration to foil electronics. These nano
- and microtechnologies are the basis for the other competence areas at Fraunhofer

EMFT:
sensor solutions, safe and secure electronics, and micropumps.

The interdisciplinary interaction of these competencies enables the development of truly novel solutions to meet the current challenges facing our society.

The Machine Learning Enhanced Sensor Systems working group is conducting research on a predictive maintenance approach for industrial plants in the ongoing project "KIWA - AI based predictive maintenance for plants in manufacturing".

A plant has already been selected at one project partner, and position and status data of the control system, as well as vibration data are being collected.

This is a non-synchronous, high dimensional and high frequency time series data set. To move towards a predictive maintenance approach, anomaly detection methods will be tested and evaluated on this data set.

Since the type of anomaly is unknown, it is important to try out different approaches and to make them comparable by means of suitable benchmarking.

We are looking for a motivated student to support our team in research and development with immediate effect.

What you will do

Selection and implementation of AI/machine learning models for anomaly detection for nonsynchronous, high dimensional and high frequency time series data.
Training of AI/Machine Learning models on computationally powerful servers
Characterization and evaluation of the selected models

What you bring to the table

You study in the field of electrical engineering and information technology, computer science or comparable field of study.
You have good knowledge of Python and have already gained some experience in the field of machine learning.
You are selfreliant and independent and enjoy working in a team.

What you can expect

We offer you an open and collegial working environment as well as a challenging and varied job with responsibility and flexible working hours that fit your studies.

At EMFT we value commitment and creativity, which is why we allow freedom for your ideas and abilities.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process.

As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.

Questions about this position will be gladly answered by:
Florian Rieger
Machine Learning Enhanced Sensor Systems

Tel:
Fraunhofer Institute for Electronic Microsystems and Solid State Technologies EMFT

Requisition Number: 67919
Application Deadline: 08/31/2023