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actors. The developed algorithms will be validated using simulation testbeds and simple hardware-in-the-loop microgrid setups with battery storage. Overall, this research will advance the state of the art
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patterns across multiple annotation types. The core aim is to generate new scientific insight by associating LCRs with their functions through a combination of expert curation and modern machine learning
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generation sequencing Applying machine learning-guided directed evolution to improve multiple enzyme properties Upscaling selected biotransformation reactions in collaboration with academic partners
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candidate will contribute to: Developing supervised deep learning algorithms for 3D point clouds Developing self-supervised deep learning algorithms for 3Dpoint clouds Expand for a wider variety of downstream
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contribute to: Developing supervised deep learning algorithms for 3D point clouds Developing self-supervised deep learning algorithms for 3Dpoint clouds Expand for a wider variety of downstream tasks focused
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network aims to deliver fiber-optic quality experiences over wireless links by building the theoretical, algorithmic, and architectural foundations of THz systems. It introduces ultra-MIMO (multiple-input
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modes (e.g., HCCI) for net-zero fuels like hydrogen and ammonia. A key innovative pillar is the development of an AI-driven control strategy. Machine learning algorithms, including reinforcement learning
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strong background in scientific computing to contribute to various algorithmic patterns in an agile development environment. Within an Agile team set up: - You will contribute according to your expertise
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park, in a dynamic ecosystem that brings together academics and companies of all sizes. The Signal team of the i3S Laboratory (https://i3s.univ-cotedazur.fr/signal ), aims to develop advanced, innovative
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for Pollinator Monitoring: Train and optimise deep learning models for pollinator detection and classification using annotated image datasets. Post-processing object tracking algorithms will be incorporated