Job Description
In today's advanced 5G networks, multiple applications share the same communication link and often compete for limited resources. For connected cars, such uncoordinated competition can render critical car-to-cloud applications (e.g., teleoperated driving) unreliable.
In this master thesis:
* You will study the state-of-the-art congestion control mechanisms for time-critical applications.
* Additionally, you will identify the metrics to monitor and optimize with QoS (Quality of Service)-aware connectivity control on multi-application devices using COTS (Commercial Off-the-Shelf) 5G equipment.
* Furthermore, you will also design and implement an intelligent connectivity control mechanism using machine learning techniques to maximize network efficiency while ensuring consistent QoS across diverse traffic types.
* Last but not least, you will compare the developed approach against a selected baseline to analyze improvements in efficiency and performance.
Qualifications
* Education: Master studies in the field of Computer Science, Computer Engineering, Electrical Engineering or comparable with good grades
* Experience and Knowledge: good knowledge of Python and Linux; basic understanding of communication and networking; experience in machine learning and scientific writing is preferred
* Personality and Working Practice: you are a communicative and structured person with analytical thinking
* Languages: professional in English
Additional Information
Start: according to prior agreement
Duration: 6 months
Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations, a link to your Git repository and if indicated a valid work and residence permit.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.
Need further information about the job?
Jo Min Hee (Functional Department)
+49 173 3420382
#LI-DNI