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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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Location: Luxembourg, Luxembourg 1511, Luxembourg [map ] Subject Area: Quantum LDPC codes with efficient decoding algorithms Appl Deadline: none (posted 2025/08/05) Position Description: Apply Position
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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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actively contribute to the WeForming and EnerTEF projects. WeForming and EnerTEF propose developing automatized and intelligent solution for operating active distributed grids with multiple active asset6s
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skills in data analysis, machine learning, as well as in mathematical and computational modelling? You will have the opportunity to investigate innovative solutions using machine learning algorithms and
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optimizing PIC algorithms for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations
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optimizing PIC algorithms for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations
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the opportunity to investigate a broad range of research areas with particular focus on understanding the foreseeable adoption of various energy technologies related to e-mobility (e.g., distributed
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security—including food distribution, nutrition, and the socio-economic drivers of inequality. Using a global lens while remaining rooted in national, regional, or local contexts, preferably in the Global
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surface properties. Many of these properties are believed to represent adaptations to specific environmental conditions, resulting in distinct distributions of certain combinations of leaf properties