Student Research Assistant (m/f/d) for battery field data analysis and machine learning application deployment

09.12.2024, Studentische Hilfskräfte, Praktikantenstellen, Studienarbeiten

Join us in the innovative KI-M-Bat research project, where we focus on optimizing the operation of modular second-life battery storage systems for grid applications. As part of the TUM-EES team, you will contribute to developing and deploying battery digital twins capable of learning from real-world field data.

• Tasks
  • Conduct data analysis of real second-life battery systems.
  • Assist in the development of data-driven models that monitor critical battery parameters.
  • Help in the deployment and integration of data-driven battery models in field applications.
• Requirements
  • Strong understanding of battery modeling principles and practices.
  • Proficient programming skills in Python and/or Julia.
  • Familiarity with IIoT application deployment, including tools such as Docker, InfluxDB, and MQTT.
  • Experience with machine learning techniques and knowledge of MLOps practices for deploying machine learning applications is advantageous.
• We offer
  • Remuneration according to the tariff regulation
  • Flexible working hours up to 20 hours per week.
  • Temporary employment contract until 30 June 2025, with an option to extend.

Kontakt: martin.cornejo@tum.de

Mehr Information

https://www.epe.ed.tum.de/fileadmin/w00bzo/ees/Stellenangebote/Studentische_Hilfskraefte/2024-11-26_Auschreibung_HiWi_-_KIMBAT_MCV.pdf