Werkstudent, Befristete Anstellung at fortiss GmbH in München
Online since 2024-05-10

fortiss is the research institute of the Free State of Bavaria for software-intensive systems and services with headquarters in Munich. The institute currently employs around 150 employees, who collaborate on research, development and transfer projects with universities and technology companies in Bavaria, Germany and Europe. Research is focused on state of the art methods, techniques and tools of software development, systems & service engineering and their application to reliable, secure cyber-physical systems, such as the Internet of Things (IoT). fortiss has the legal structure of a non-profit limited liability company (GmbH). Its shareholders are the Free State of Bavaria (as majority shareholder) and the Fraunhofer Society for the Promotion of Applied Research.

To further strengthen our Architectures and Services for Critical Infrastructures team, we are looking for a new team member:

Student Assistant for Development of digital twin and data-driven models for energy grid applications (m/f/d) up to 20h/week

In recent years, the landscape of energy distribution systems has undergone a remarkable transformation. The modernization and diversification of energy networks has come with severe challenges and opportunities, and the advent of smart grids has initiated an era of massive data generation and exchange in this domain. Data from several measuring devices distributed across the network is now available, and this together with the power of modern machine learning techniques present opportunities for a more resilient, efficient, and sustainable energy infrastructure.

The aim of the project is to develop an automated process for robust and accurate fault detection and diagnosis in low and medium-voltage grids, including load forecasting. To this end, learning methods involving external knowledge sources must be researched for accurate and timely data-driven fault localization in medium and low voltage networks based on a digital twin. In addition, robust approaches ensure reliable localization even when measurement data is missing or inaccurate. Our research relies on data from heterogeneous measuring devices at different locations in the grid for fault localization.

Your tasks:

• Further Development of digital twins for low and medium voltage energy grids (Matlab/Simulink/Simscape)
• Scaling up and parallelizing simulations and training e.g. via Cloud computing, Containerization and Virtualization Techniques (Cloud/Docker/Kubernetes/VMs)
• Scripting (Python/Bash)
• Developing and improving Neural Network Models for forecasting, detection and localization (Python/Pytorch/Matlab)
• Code Versioning and Issue Management (Git)
• Potential training and co-developing new techniques and ML models
• Networking (TCP/IP/UDP)
• Potential co-authoring research papers and supporting dissemination tasks.

Your profile:

• Student of B. Sc. / M. Sc. in Computer Science, Electrical Engineering or similar
• Software development experience
• Linux experience (Desirable)
• Knowledge in neural networks, machine-learning
• Self-motivated and structured way of working
• Good communication skills in English
• German communication skills (Desirable)
• Availability to work on-site

Our offer:
• International and dynamic work environment
• Flexible schedule and work in convenient location
• Possibility to perform research in challenging and exciting topics in the field of machine learning and digital twins in the energy grid domain
• Possibility to extend to Bachelor’s or Master’s thesis

Did we catch your interest?

Please submit your application with a motivational statement, a detailed CV and a current transcript of records.

Job-ID: ASCI-SH-03-2024
Contact: Camilo Amaya Rodriguez

Link to the offer Applications to this email address

Alumni & Career übernimmt keine Gewähr für die Inhalte dieser Stellenanzeige. Sehen Sie ein Problem mit dieser Stellenanzeige? Report this job ad!

Cookies are used on this website collect usage statistics (optional cookies) and to enable login (essential cookies).