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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria The position requires a PhD in machine learning, NLP, causality, or a related discipline, with a strong command of deep
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 19 days ago
disseminate the developed methods. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2025-09574 Requirements Skills/Qualifications PhD in Computer Science, Machine Learning, Bioinformatics
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Requirements Research FieldComputer science » Computer systemsEducation LevelPhD or equivalent Skills/Qualifications Knowledge • Solid understanding of machine learning, deep learning, and modern AI techniques
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Post-doctoral Researcher in Multimodal Foundation Models for Brain Cancer & Neuro-degenerative Disea
Qualifications PhD in machine learning, computer vision or a related field. Established expertise in deep learning methods applied to images analysis. Experiences with generative models, volumetric image
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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collaborate with global leaders (e.g., Mediterranean Institute of Oceanography, Southern Denmark University). - Publish high-impact research as first author, present at international conferences, mentor
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, France [map ] Subject Areas: Machine Learning / Machine Learning Statistics Mathematics Probability Statistical Physics Appl Deadline: 2025/12/20 11:59PM (posted 2025/11/25, listed until 2026/05/25
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on the plants Arabidopsis thaliana will generate maps of depolarization, retardance, dichroism, and optical axis azimuth, which will feed machine learning models developed by the project partners to identify
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dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling
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reliability. · Understanding of hardware accelerators for AI and their operation. · Familiarity with machine learning workloads (e.g., CNNs). As this is a research position, it is necessary