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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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machine learning or trustworthy AI, including experience with robustness assessment and attack/defense mechanisms. Expertise in software security and code analysis, with understanding of common
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and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project tackles key challenges in anomaly detection, transaction classification, and
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proof-of-concept software tools Machine learning is a plus Strong analytical and programming skills are required (Python, Matlab, and C/C++). Prior proven experience in data-driven innovation projects is
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driving, in-car monitoring, industrial automation, and security surveillance. The research, called "R4DAR," aims to leverage emerging 4D imaging technology with Massive MIMO to create image-like radar
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of metabolic network modelling linked to epigenetics Carry out machine learning, and integrative analysis of large epigenome datasets Communicate research results in international conferences and journals Work
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-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put on discovering biophysical
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framework to bridge this gap and enable organizations to confidently deploy secure GenAI solutions by evaluating the machine-learning models intrinsically, identifying components of an AI pipeline and their
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-connectivity Communication system modelling, performance analysis, and simulation. Optimization tools and machine learning techniques. Hands-on experience with software-defined radios (SDRs) and/or
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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, and Machine Learning/AI 5G on organisations from both the private and public sectors