Master Thesis in Data Science: Content-Aware Data Compression for Distributed Acoustic Sensing
vor 3 Wochen
WE ARE AGAIN AWARDED AS TOP ARBEITGEBER 2025 At AP Sensing, we're building on a success story that began in the garage of Bill Hewlett and Dave Packard. As an independent HP spin-off, we combine over 40 years of technological expertise with a passion for innovation. Together we'll continue to enhance infrastructure, protect people, and safeguard our planet. With a commitment to fiber optic sensing, we value every voice and empower growth. Join us in thinking ahead to create a smarter, safer world. Distributed Acoustic Sensing (DAS) enables long-range and high-resolution measurements along optical fibers by transforming them into arrays of thousands of virtual microphones. This technology powers a wide range of applications, including critical infrastructure monitoring and environmental sensing. However, DAS systems generate massive volumes of high-resolution data, creating substantial challenges for storage, transmission, and real-time processing. Develop a content-aware data compression method for Distributed Acoustic Sensing (DAS) data that dynamically adapts to the characteristics of the signal to maximize data reduction while preserving essential information required for downstream machine learning (ML) tasks. Evaluate the developed method using benchmark DAS datasets containing various events and activities. Train and validate ML models for event classification using the decompressed data. Integrate a working software prototype into AP Sensing's data extraction pipeline, supporting cloud-based storage and transmission. You are a master student and are looking for a company for your master's thesis You are interested in the topic: "Content-Aware Data Compression for Distributed Acoustic Sensing" Academic background in Computer Science, Mathematics, Physics, or Electrical Engineering Strong programming skills in Python and/or Rust Good knowledge of signal processing and machine learning Interest in applied research and real-world data challenges Duration: 6 months/20h per week Flexibility: Flexible working hours that are compatible with your studies Team: A motivated and friendly team that supports and encourages you Location: only a 2-minute walk from the S-Bahn station with a direct connection to Stuttgart Office space: You will work on site in large, bright and air-conditioned offices with height-adjustable desks and a fully equipped kitchen with a high-tech coffee machine.
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