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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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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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systems. The position offers an exciting opportunity to engage in cutting-edge research that addresses both theoretical challenges and practical applications. Key Responsibilities: Develop innovative
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heterogeneous multi-omics datasets. Integrative Data Analysis: Perform and lead analysis of large-scale multi-omics datasets, including RNA/DNA sequencing, methylation, and metabolomics. Method Development
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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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Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial
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del Sol, which develops computational models to address challenges in stem cell research, aging and disease modeling. The group employs methodologies from different areas of mathematics, engineering
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. Finally, the research will develop efficient algorithms and test them on realistic networks and using real data from energy and public transport operators. The Doctoral student is also expected