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or BSc + one year of Master Studies in mathematical engineering, mathematics, computer science, electrical engineering or similar. Solid mathematical and analytical skills, including optimization and/or
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. degree in computer science, mathematical engineering, mathematics, or similar; - Solid mathematical and analytical skills, including mathematical optimization and information theory; - Solid
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to explore GNSS Reflectometry (GNSS R) as a novel, low cost, low power bistatic remote sensing technique optimized for nanosatellite platforms. GNSS R leverages signals of opportunity from existing
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, mathematical engineering, acoustics, machine learning or similar; Solid mathematical and analytical skills, including signal processing, optimization, machine learning or information theory; Experience in
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competencies Applicants are required to have an excellent academic background with a master’s degree or equivalent in Energy Engineering, Electrical Engineering, Mathematics, Control theory, or any other related
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, Mathematics, Control theory, or any other related discipline, potentially with skills in Power Electronics Converters and Control. Applicants who are in the final phase of their master’s degree are also
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engineering, or similar. Solid mathematical and analytical skills, including signal processing and optimization. Knowledge about classical and/or quantum data communication, including for instance error
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algorithmic solution development. The group focuses particularly on automated decision-making in autonomous cyber-physical systems, combining mathematical optimization, machine learning, and decision theory
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sensor integration. Experience with SLAM algorithms (vision-, acoustic-, or inertial-based), state estimation (e.g. Kalman filtering, pose graph optimization), or collaborative positioning is highly valued