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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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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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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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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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and image analysis (MATLAB or Python), machine learning techniques, and basic programming/coding will be a plus. Fluency in English is mandatory. Willingness to work in an inter-cultural and
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machine learning methods to investigate how ecosystem water stress and drought disturbances affect relevant forest ecosystem functioning at various scales. It will enable advanced assessment of forest
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, mathematics, physics, remote sensing and machine learning. Experience and skills · Strong interest in modelling, model-data integration, and remote sensing data analysis. · Knowledge of programming, remote
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application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial peptides Perform and support experimental studies across the METAMIC project
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21 Aug 2025 Job Information Organisation/Company University of Luxembourg Research Field Computer science » Computer systems Researcher Profile First Stage Researcher (R1) Country Luxembourg
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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