This Master Thesis focuses on designing and implementing an FMCW radar DSP pipeline for monitoring home appliance operating states. Responsibilities include sensor characterization, calibration, range/Doppler processing, clutter suppression, beam forming, feature extraction, and developing lightweight ML algorithms and neural networks. The role involves algorithm and model development, benchmarking, documentation, and presenting results, with potential for embedded platform prototyping.

Must Have

• Currently a master’s student in Electrical Engineering, Computer Science, Physics, Applied Mathematics, or related, at thesis stage
• Strong background in DSP and radar systems (FMCW preferred), including FFT-based processing, filtering/windowing, and beamforming
• Proficient in Python
• Experience with AI algorithms applied to sensor data (lightweight models preferred) & common ML/DSP libraries
• Good skills in English

Good to Have

• German language skills

Perks & Benefits

• Career development & learning (About 17,000 free LinkedIn Learning courses in 10 languages, Mentoring & Coaching programs, Global Onboarding process as well as Virtual & f2f trainings)
• Time & flexibility (Flexible working hours & part-time work, Mobile work, Remote work from abroad / Workation (EU), Sabbatical (EU))
• Health & wellbeing (Health activities & programs, Health & Well-being LinkedIn Learning Journey, Health & Well-being Community)
• Your working environment (Employee events, Open & global community, Open office spaces)
• Rewards & benefits (Fair and transparent remuneration, Success participation, Performance-related recognition, customised benefit packages)
• Location specific benefits

Your Role

This MSc thesis focuses on FMCW radar sensing, signal processing, and lightweight AI algorithms for monitoring operating states of home appliances.

Key responsibilities in your new role

• Shape the future: Design and implement the radar DSP pipeline end to end - sensor characterization and calibration, range/Doppler processing, clutter suppression, beam forming, feature extraction, and lightweight ML algorithms and neural networks
• Keep the overview: The work includes algorithm and model development, benchmarking, documentation, and presenting results to technical stakeholders, with the opportunity to prototype deployment on embedded platforms

Your Profile

Qualifications and skills to help you succeed

• Study field: You are currently a master’s student in Electrical Engineering, Computer Science, Physics, Applied Mathematics, or related, at thesis stage
• Experience & Interest: Strong background in DSP and radar systems (FMCW preferred), including FFT-based processing, filtering/windowing, and beamforming
• Skills: Proficient in Python; experience with AI algorithms applied to sensor data (lightweight models preferred) & common ML/DSP libraries
• Language: Good skills in English; German is a plus

Please attach the following documents to your application:

• CV in English
• Certificate of enrollment at university
• Excerpt of the study regulations for the thesis (if applicable)
• Latest grades transcript (not older than 6 months)
• High school report

Contact:

Mafalda Porto

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• Find out how the student application process works with us
• Discover our student website

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