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research unit in computer science and information technologies in France. ICI is one of ETIS's four multi-PI groups and is specialized in signal processing, information theory and wireless communications
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hold a PhD in cell or developmental biology, with possible previous experience in microscopy and quantitative imaging. The successful candidate will be expected to work in an interdisciplinary and
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compatible with high-speed imaging over long durations, (2) optimize system control and develop signal processing tools, and (3) apply it to imaging challenges involving organoids and living biological tissues
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learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing and implementing NLP pipelines for clinical text processing, semantic annotation
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model characterisation strategies definition of the necessary equipment definition of the necessary processing algorithms Implementation/validation/drafting of scientific articles in collaboration with
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hardware-friendly inference solutions - participate to publication and valorization of these works Where to apply E-mail job-ref-42dqy7g9pn@emploi.beetween.com Requirements Research FieldEngineeringEducation
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emulator of sea ice dynamics, trained using high-fidelity numerical simulations, (ii) variational data assimilation methods, and (iii) a simplified representation of physical processes in the atmospheric
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preparation platform, SSMIM - Resources provided (equipment, IT, etc.): Computer workstation, binocular magnifying glass, analytical equipment (IRMS mass spectrometer) Where to apply Website https
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Language Processing, can be used to extract linguistic knowledge, particularly concerning tones. The main object of investigation will be the Yongning Na language (ISO code: NRU). This work, based on fine-tuning models
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sodium batteries. High-power operation increases the risk of early battery degradation. These degradations can be observed in detail when the batteries are opened (post-mortem analyses) or through invasive