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genomic graphs. The project will also deliver efficient algorithms to train these models under budget and time constraints, facilitating flexible adoption of the methods. The project is carried out in close
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validation (V&V) techniques for space systems, software and algorithms with a focus on specific challenges of space-borne perception and proximity operations uncooperative spacecraft . Develop novel methods
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vulnerability assessment for satellite communications links Design, implement, and evaluate AI/ML‑based algorithms for real‑time detection, classification, and localization of jamming and spoofing signals using
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/sigcom . In addition, SIGCOM hosts the following experimental facilities: 6GSpaceLab: Real-world testing and validation of advanced communication algorithms CommLab: Real-time wireless testbeds for DSP in
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by integrating large-scale single-cell foundation models with structured biological knowledge encoded in genomic graphs. The project will also deliver efficient algorithms to train these models under
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methods and tools for adaptive, AI-driven embedded algorithms to support real-time analytics and decision-making in IoT, wireless communication, and remote sensing systems Collaborate with multidisciplinary
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datasets (e.g., RNA-Seq, epigenetics, cancer genomics, or metabolomics). Statistical Knowledge: Strong understanding of algorithmic, statistical, and ontological concepts in biology. Communication: Excellent
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3GPP compliant 5G/6G NR NTN OFDM waveforms Develop and analyse signal processing and/or machine learning algorithms for joint channel, delay, Doppler and carrier phase estimation, remote object ranging
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into clinically actionable biomarkers and decision-support tools that underpin the project's personalized treatment algorithms. For further information, please contact Prof. Dr. Antonio del Sol, antonio.delsol
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using both physical and machine learning-based algorithms. Strong interpersonal and communication skills and the ability to work both independently and collaboratively with researchers and students from