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significant computational component. We strongly recommend a background in machine learning and coding. Applicants with a background in areas such as computational neuroscience, reinforcement learning, or deep
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tasks and zero-shot evaluation in linguistic analysis. Profile • Master’s degree (M2) or PhD in computer science, NLP, machine learning, deep learning, or a related field. • Strong experience in machine
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Qualifications/knowledge : PhD in computer science, with a specialisation in computer vision, digital geometry processing and/or machine learning. No specific knowledge about plants is required. Operational skills
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 14 days ago
Essential qualifications: Advanced degree (Master's or PhD) in Computer Science, AI, Machine Learning, or related field Demonstrated expertise in large-scale generative AI systems (inference and training
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processing, visualization. You will explore new avenues in coherent imaging, e.g. exploiting machine learning or introducing new techniques exploiting the EBS-enhanced coherent photon flux. You will also
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Researcher (R1) Positions PhD Positions Country France Application Deadline 29 Dec 2025 - 12:00 (Europe/Paris) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research
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. 5, no. 2, pp. 354–379, 2012. [2] C. K. Williams and C. E. Rasmussen, Gaussian processes for machine learning. MIT press Cambridge, MA, 2006, vol. 2, no. 3. [3] G. Daras, H. Chung, C.-H. Lai, Y
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science » Computer systemsEducation LevelPhD or equivalent Skills/Qualifications The selected candidate must have a PhD in Computer Science and demonstrate strong teaching and pedagogical skills, supported
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Apprenticeship Status (FISA); Specialized training (master's degrees, specialized master's degrees, continuing education); Doctoral training. Your expertise in applied mathematics, machine learning, and biosignal
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machine learning to model network behavior from real-world measurements (e.g., [7]). Although promising, these approaches still face three major limitations: (i) they often rely on idealized and extensive