PhD Student – Energy-Efficient Communication Systems for Next-Generation CubeSats Interdisciplinary Transformation University (IT:U) in Austria

The Interdisciplinary Transformation University (IT:U) in Austria invites applications for a PhD position focused on “Energy-Efficient Communication Systems for Next-Generation CubeSats.” This opportunity is part of the IT:U Doctoral School’s COMPUTATIONAL X PhD program and is based in the Smart Sensing and Systems Lab (S³ Lab) in Linz. The position is full-time, up to 4 years, and starts in March 2026 under the supervision of Fellow Professor Michele Magno.

The research centers on developing innovative embedded solutions for CubeSat communications, emphasizing efficient uplink/downlink design, low-power communication protocols, and real-time optimization for reliable data transfer in space-constrained environments. The successful candidate will work at the intersection of embedded systems, communication, and smart wireless sensing, with applications in autonomous robotics, drones, and space technologies.

Key research directions include energy-efficient communication protocols, cross-layer optimization for CubeSats, embedded machine learning (including spiking and non-spiking networks), neuromorphic computing, TinyML, Edge AI, and advanced signal processing techniques. The project aims to enable reliable, low-power, and intelligent space communications for Earth observation and related applications.

IT:U offers a dynamic, interdisciplinary research environment with opportunities to collaborate on cutting-edge CubeSat prototypes and real-world space demonstrators. The university provides a strong academic and industrial network across embedded AI, space sensing, and Earth observation technologies. The position includes a competitive gross salary starting from EUR 3,714.80/month (full-time, 40h) and the Austrian KlimaTicket Ö for unlimited public transportation within Austria.

Applicants should have a master’s degree or equivalent in electrical engineering, computer science, or a related field, with strong programming skills and experience in hardware-software co-design. Familiarity with deep learning frameworks (TensorFlow, PyTorch), spiking neural networks, and compartmental models is advantageous. Fluency in English (CEFR C1 or equivalent) is required.

The structured 4-year PhD program includes focused group work, research lab modules, and interdisciplinary seminars, culminating in a PhD thesis and defense. The application deadline is January 31, 2026. For more information about the lab, visit https://itu-s3-lab.github.io.

To apply, complete the online form at https://apply.it-u.at/ and upload your CV, diplomas, transcripts, motivational letter, and up to three recommendation contacts. For questions or expressions of interest, contact Prof. Michele Magno at [email protected] before submitting your application.