Job Description:
Are you looking for a master’s thesis position?
Are you up to discover and inspire advanced processing solutions using Artificial Intelligence on neuromorphic hardware for next-generation Telecom Satellites? Do you want to be part of the group of leading experts in this field? We look forward to you joining us within Airbus Defence and Space, Digital Payload Hardware Product Design Germany for a
Master Thesis (d/m/w) for neuromorphic on-board processing for Telecom Satellites
You will support the department of Digital Payload Hardware Product Design Germany which specializes in high performance processing solutions for satellites. The R&T focus are satellite on-board machine learning applications for radio frequency (RF) communication and anomaly detection scenarios, as well as edge computing and future 5G/6G payloads on novel, space-grade AI-optimized processing hardware.
Your location
Our site is just a stone's throw away from Munich, the beautiful capital of Bavaria. Are you into sports and other outdoor activities? The Alps and Lake Starnberg are within an hour’s reach, offering a multitude of recreational options.
Your benefits
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Attractive salary and work-life balance with an 35-hour week (flexitime).
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International environment with the opportunity to network globally.
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Work with modern/diversified technologies.
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At Airbus, we see you as a valuable team member and you are not hired to brew coffee, instead you are in close contact with the interfaces and are part of our weekly team meetings.
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Opportunity to participate in the Generation Airbus Community to expand your own network.
Your tasks and responsibilities
Neuromorphic processing has emerged as a hot-topic for Machine Learning inference on space-based systems. Spiking neural networks (SNNs) can in principle be applied to the same applications as traditional Artificial Neural Networks (ANNs) but promise to run more power efficiently, which is a major constraint on power-limited satellites. However, this technology is highly novel and various steps are required before bringing it to the satellite. These steps include:
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Use neuromorphic processing evaluation hardware
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Translate “classic” ANNs to SNNs
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Develop and train SNNs for Earth Observation data and time-series data
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Compare key metrics (e.g., power efficiency, computational power, etc.) to other state-of-the-art space-grade ML hardware platforms
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Research about on-board continuous, online learning
With this position you will focus on acquiring and building up knowledge on how to effectively employ neuromorphic processing solutions from the perspective of space-based deployment scenarios. The goal is to demonstrate space-targeted AI algorithms on real evaluation hardware.
Desired skills and qualifications
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Enrolled full-time student within background in Artificial Intelligence, Telecommunication Engineering, Electrical Engineering, or similar field of study
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First Experiences in inference of Neural Networks on embedded processing platforms (CPU, FPGA) would be an asset
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Embedded programming skills (Python, C, C++)
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Deep Neural Network modelling skills with common frameworks (Tensorflow, PyTorch)
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Fluent English is mandatory; German would be an asset
Please upload the following documents: cover letter, CV, relevant transcripts, enrollment certificate.
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This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.
Company:
Airbus Defence and Space GmbH
Employment Type:
Final-year Thesis
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Experience Level:
Student
Job Family:
Support to Management
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