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Master’s Thesis Adaptive Multi-Modal Sensor Fusion with 5G/6G ISAC

Bosch·ErlangenHybridFull-time
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Vox-Zusammenfassung
  • Aufgaben: Analyse der ISAC-Sensorik, Entwicklung eines modularen Simulators, Gestaltung und Implementierung adaptiver Sensorfusion, Vergleich verschiedener Wahrnehmungssysteme, Durchführung von Simulationen.
  • Anforderungen: Master-Studium in Elektrotechnik, Kommunikations-, Robotik- oder Informatik, Kenntnisse in Signalverarbeitung, Kommunikation, Sensorfusion, Erfahrung mit Matlab, Verständnis für 5G/6G, Radar, Wahrnehmungssysteme, Kenntnisse in Machine Learning sind vorteilhaft.
  • Bedingungen/Vorteile: 6-monatige Laufzeit, hybrid Arbeitsmodell, Möglichkeit in Büro und remote zu arbeiten, Interesse an Sensor- und Mobilitätstechnologien, sehr gute Englischkenntnisse, Einschreibung an Universität erforderlich.
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Stellenbeschreibung

The future of automated driving and driver assistance systems is being significantly shaped by innovative sensor technology. Are you ready to redefine the boundaries of what is possible at the intersection of wireless communication and high-precision environmental perception? With this master's thesis, you will dive deep into the pioneering field of Integrated Sensing and Communication (ISAC) and play an active role in shaping how next-generation vehicles perceive their surroundings. • During your thesis, you analyze the role and limitations of ISAC sensing for vehicle environment perception. • Furthermore, you develop or extend a modular system-level simulator—for instance, based on Matlab for multi-sensor scenarios. • You work on designing as well as implementing adaptive sensor fusion strategies, such as modality weighting, selection, or switching. • In Addition, you compare various perception pipelines, including purely radar-based systems, classical multi-sensor systems, and ISAC-enabled approaches. • You conduct simulation experiments under varying environmental and system conditions. • Lastly, you interpret the results regarding robustness, reliability, and system-level trade-offs. • Education: Master studies in Electrical Engineering, Communications Engineering, Robotics, Computer Science, or related fields   • Experience and Knowledge: solid knowledge of signal processing as well as communications engineering or sensor fusion; experience with Matlab or comparable simulation tools; understanding of wireless systems (5G/6G), radar, or perception systems; knowledge of machine learning is an advantage • Personality and Working Practice: you are able to handle complex technical issues independently and with a high level of attention to detail, consistently meeting deadlines and demonstrating a strong sense of responsibility; you bring a proactive attitude and an interest in sound reasoning and evaluation at the system level • Work Routine: with us, you benefit from a hybrid model that enables you to work both in the office and remotely • Enthusiasm: you bring a genuine interest in next-generation sensor technology, 6G technologies, and intelligent mobility systems • Languages: very good in English 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 and if indicated a valid work and residence permit. You are almost finished with your Bachelor's degree and would like to gain some practical experience before embarking on your next academic adventure with a Master's degree? Then you fit in perfectly well with our PreMaster Programm! Take a look at our vacancies here. 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? Hussein Ali Abdulhadi Al-Hakeem (Hiring Manager) Work #LikeABosch starts here: Apply now! #LI-DNI

Transparenz-Panel

Originalquelle
smartrecruiters
Veröffentlicht
23. Juni 2026 · echtes Datum
Zuletzt verifiziert
gestern
Qualitäts-Score
70/100
Gehalt angegeben0
Unternehmen identifiziert25
applyUrl10
postedAt15
Vollständige Beschreibung20

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